Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding - Volume 151 Frontiers in Artificial Intelligence and Applications

LEARNING BY EFFECTIVE UTILIZATION OF TECHNOLOGIES: FACILITATING INTERCULTURAL UNDERSTANDING

Frontiers in Artificial Intelligence and Applications FAIA covers all aspects of theoretical and applied artificial intelligence research in the form of monographs, doctoral dissertations, textbooks, handbooks and proceedings volumes. The FAIA series contains several sub-series, including “Information Modelling and Knowledge Bases” and “Knowledge-Based Intelligent Engineering Systems”. It also includes the biennial ECAI, the European Conference on Artificial Intelligence, proceedings volumes, and other ECCAI – the European Coordinating Committee on Artificial Intelligence – sponsored publications. An editorial panel of internationally well-known scholars is appointed to provide a high quality selection. Series Editors: J. Breuker, R. Dieng-Kuntz, N. Guarino, J.N. Kok, J. Liu, R. López de Mántaras, R. Mizoguchi, M. Musen and N. Zhong

Volume 151

Recently published in this series Vol. 150. B. Bennett and C. Fellbaum (Eds.), Formal Ontology in Information Systems – Proceedings of the Fourth International Conference (FOIS 2006) Vol. 149. X.F. Zha and R.J. Howlett (Eds.), Integrated Intelligent Systems for Engineering Design Vol. 148. K. Kersting, An Inductive Logic Programming Approach to Statistical Relational Learning Vol. 147. H. Fujita and M. Mejri (Eds.), New Trends in Software Methodologies, Tools and Techniques – Proceedings of the fifth SoMeT_06 Vol. 146. M. Polit et al. (Eds.), Artificial Intelligence Research and Development Vol. 145. A.J. Knobbe, Multi-Relational Data Mining Vol. 144. P.E. Dunne and T.J.M. Bench-Capon (Eds.), Computational Models of Argument – Proceedings of COMMA 2006 Vol. 143. P. Ghodous et al. (Eds.), Leading the Web in Concurrent Engineering – Next Generation Concurrent Engineering Vol. 142. L. Penserini et al. (Eds.), STAIRS 2006 – Proceedings of the Third Starting AI Researchers’ Symposium Vol. 141. G. Brewka et al. (Eds.), ECAI 2006 – 17th European Conference on Artificial Intelligence Vol. 140. E. Tyugu and T. Yamaguchi (Eds.), Knowledge-Based Software Engineering – Proceedings of the Seventh Joint Conference on Knowledge-Based Software Engineering Vol. 139. A. Bundy and S. Wilson (Eds.), Rob Milne: A Tribute to a Pioneering AI Scientist, Entrepreneur and Mountaineer ISSN 0922-6389

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding Edited by

Riichiro Mizoguchi Osaka University, Japan

Pierre Dillenbourg

Swiss Federal Institute of Technology Lausanne, Switzerland

and

Zhiting Zhu

East China Normal University, China

Amsterdam • Berlin • Oxford • Tokyo • Washington, DC

© 2006 The authors. All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without prior written permission from the publisher. ISBN 1-58603-687-4 Library of Congress Control Number: 2006936136 Publisher IOS Press Nieuwe Hemweg 6B 1013 BG Amsterdam Netherlands fax: +31 20 687 0019 e-mail: [email protected] Distributor in the UK and Ireland Gazelle Books Services Ltd. White Cross Mills Hightown Lancaster LA1 4XS United Kingdom fax: +44 1524 63232 e-mail: [email protected]

Distributor in the USA and Canada IOS Press, Inc. 4502 Rachael Manor Drive Fairfax, VA 22032 USA fax: +1 703 323 3668 e-mail: [email protected]

LEGAL NOTICE The publisher is not responsible for the use which might be made of the following information. PRINTED IN THE NETHERLANDS

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

v

Preface Welcome to the 14th International Conference on Computers in Education (ICCE) hosted by Beijing Normal University (BNU) and supported by the Ministry of Education (MOE) and Chinese Association for Artificial Intelligence (CAAI). The series of conferences started in Taiwan in 1989. The three first editions of ICCE occurred there and then the conference moved across many countries in the Asia-Pacific region: China, Singapore, Malaysia, Japan, Korea, New Zealand, Hong Kong and Australia. The ICCE series is organized by the Asia-Pacific Society for Computers in Education (APSCE). The ICCE is an international event, with strong participation from researchers from Asia and Oceania. Since the beginning, there has also been strong involvement from researchers in Europe and North-America. These proceedings include the 45 full papers and 53 short papers that have been selected by an international board of scholars listed hereafter. All papers have been reviewed by three or at least two reviewers and double-checked by the Co-chairs of the programme committee. Altogether 254 papers were submitted, which represents a rate of acceptance of 18% if considering only long papers or 38% if including short papers. The content of this volume hence results from a severe selection process. In addition, 22 posters will be presented at the event. A major theme of this conference is the use of computers for supporting collaborative learning. This is not surprising since computer-supported collaborative learning has become both a widespread educational practice and a main domain of research. Moreover, collaborative learning has deep roots in Asian educational traditions. Given the large number of researchers within this field, its scope has become very broad. Under this umbrella, one finds a variety of more specific topics such as: interaction analysis, collaboration scripts (e.g. the Jigsaw script), communities of practice, sociocognitive conflict resolution, cognitive apprenticeship, various tools for argumentation, on-line discussion or collaborative drawing tools (whiteboards), collaborative writing and the role of facilitators. Most research work on collaborative learning focuses on interactions rather than on the contents of environments, which had been the focus in the previous decades of learning technology research. However, there is no reason to focus on one aspect to the detriment of the other. Hence, we are quite pleased that the selected papers also cover multiple issues related to the storage, representation and retrieval of knowledge: ontologies for learning environments and the semantic web, knowledge bases and data mining, meta-data and content management systems, and so forth. These proceedings also reveal a growing interest for non-verbal educational material, namely pictures and video materials, which are already central to new popular web-based applications. Interestingly, these proceeding include contributions that bridge both research tracks, the one focusing on interactions and the other on contents: the pedagogical use of digital portfolios, both for promoting individual reflections and for scaffolding group interactions. Another specificity of these proceedings, maybe due to regional policies, is the salience of language learning within the range of educational contents covered by learning technologies.

vi

Among the other research trends that appear in the set of contributions, we find the educational use of mobile technologies and the design of educational games. The use of mobile devices and games opens new ways to think about learning technologies, namely the fact that tools that do not a priori appear as learning tools have perhaps more chances to enter into schools. There are nonetheless a limited number of papers on these issues since new conferences emerged in our community, which are devoted to these two subsets of learning technologies. Finally, let us stress the truly interdisciplinary character of this volume. It contains contributions from the field of computer science, psychology and educational sciences, three fields that are sometimes bound together under the label of ‘learning sciences’. The very positive aspect is that computational, pedagogical, cognitive or social factors are not only treated by different papers, but, in many cases, tackled within the same paper. It is our great pleasure to have gathered these rich contributions within this volume. We hope all readers will share our enthusiasm for this exceptional event. Program Committee Co-Chairs Riichiro Mizoguchi, Osaka University, Japan Pierre Dillenbourg, Swiss Federal Institute of Technology Lausanne, Switzerland Zhiting Zhu, East China Normal University, China

vii

Program Committee Conference Chair Fong-Lok Lee, The Chinese University of Hong Kong, Hong Kong Program Co-Chairs Riichiro Mizoguchi, Osaka University Pierre Dillenbourg, Swiss Federal Institute of Technology Lausanne Zhiting Zhu, College of Educational Sciences, East China Normal University Committee Members Lora Aroyo, Eindhoven University of Technology, The Netherlands Nelson Baloia, Chille University, Chile Jacqueline Bourdeau, Tele-universite du Quebec, Canada Tak-Wai Chan, National Central University, Taiwan Yam San Chee, National Institute of Education, Singapore Jim Greer, University of Saskatchewan, Canada Paivi Hakkinen, University of Jyvaskyla, Finland Yusuke Hayashi, Osaka University, Japan Lyn Henderson, James Cook University, Australia Tsukasa Hirashima, Hiroshima University, Japan Chris Hoadley, Penn State University, USA Ulrich Hoppe, University Duisburg, Germany Ronghuai Huang, Beijing Normal University, China Mitsuru Ikeda, JAIST, Japan Michael Jacobson, Nanyang Technological University, Singapore Akihiro Kashihara, The University of Electro-Communications, Japan Yong-Se Kim, Sungkyunkwan University, Korea Gee Kin Yeo, National University of Singapore, Singapore Kinshuk, Massey University, New Zealand Paul Kirschner, University of Utrecht, The Netherlands Piet Kommers, University of Twente, The Netherlands Fong-Lok Lee, The Chinese University of Hong Kong, Hong Kong Insook Lee, Sejong University, Korea Mun Kew Leong, Institute for Infocomm Research, Singapore Robert Lewis, University of Lancaster, UK Chee-Kit Looi, National Institute of Education, Singapore Tatsunori Matsui, Waseda University, Japan Gordon McCalla, University of Saskatchewan, Canada Naomi Miyake, Chukyo University, Japan Kiyoshi Nakabayashi, NTT Resonant, Inc., Japan Eugenia Ng, Hong Kong Institute of Education, Hong Kong Hiroaki Ogata, University of Tokushima, Japan Ron Oliver, Edith Cowan University, Australia Michael Sharples, University of Nottingham, UK

viii

Kathy Sinitsa, International Research and Training Center of Information Technologies and Systems, Ukraine Raymund Sison, De La Salle University, Philippines Amy Soller, Institute for Defense Analyses, Italy Gerry Stahl, Drexel University, USA Dan Suthers, University of Hawai’i at Manoa, USA Seng Chee Tan, Nanyang Technological University, Singapore Pierre Tchounikine, Université du Maine, France Qiong Wang, Peking University, China Lu Wang, Department of Educational Technology, Capital Normal University, China Yoneo Yano, Tokushima University, Japan Shelley Shwu-Ching Young, National Tsing Hua University, Taiwan Fu-Yun Yu, National Cheng-Kung University, Taiwan Shengquan Yu, College of Educational Technology, Beijing Normal University, China

ix

Additional Reviewers TSE Wing-Cheung Alex Liesbeth Baartman Ryan S.J.D. Baker Vincent Barre Moushir Bishouty Lars Bollen Chris Brook Christopher Brooks Ole C. Brudvik Jan M. Van Bruggen Murat Cakir Hyun Jin Cha Sung-Bin Chang Elizabeth S. Charles Elizabeth Charles Yu-Fen Chen Nancy Yu-Chien Chen Weiqin Chen Zhi-Hong Chen Emily Ching Winnie Chow Catherine Cleder Steve Corich Charlie Cox Nathanael Ng Hsien Ern Stuart Garner Giulia Gelmini Bas Giesbers Sabine Graf Yoshiaki Hada Kim Hagen Andreas Harrer Elizabeth Hartnell-Young Shinobu Hasegawa Toshihiro Hayashi Tomoya Horiguchi Chris Houser Gan Li Hui Yang Hui Sebastien Iksal Wai-Hung Ip Hyunju Jeung

Morris S.Y. Jong Laltitha Jonnavithula Ilkka Jormanainen Marco M.A. Kalz Toshinobu Kasai Fengfeng Ke Tomoko Kojiri Siu Cheung Kong Oskar Y.M. Ku Hidenobu Kunichika Pierre Laforcade Richard Chih-Hung Lai Yiu Chi Lai Joey Lee Youngmin Lee Michael Hung-Liang Lee Joey J. Lee Kai Ming Li Yanyan Li Calvin Liao Taiyu Lin Yimei Lin Chiu-Pin Lin Weng-Jeng Liu Joe Luca Wai Wing Ada Ma Noriyuki Matsuda Noboru Matsuda Kenji Matsuura Mark Mcmahon Takahiko Mendori Carrie E. Miller Hiroyuki Mitsuhara Akiyoshi Miyatake Yasuo Miyoshi Tomiaki Morikawa Martin Muhlpfordt Manabu Nakamura Nathanael H.E. Ng Trang Nguyen Youji Ochi Ryo Okamoto

Shuji Okamura Josephine Pelleu-Tchetagni Dominique Py David Raymond Valerie Renault Raul Morales Salcedo Johann W. Sarmiento Hong Khai Seng Junjie Shang Fawaz Shareef Hajime Shirouzu Kati Maekitalo-Siegl Oyvind Smestad Hyo-Jeong So Chiann-Ru Song Soonshik Suh Kazumoto Tanaka Philippe Teutsch Patricia Thornton Satoshi Togawa Ramon Prudencio S. Toledo Yuen Tsui Michael Verhaart Jen-Hang Wang Eric Wang Astrid Wichmann Lung-Hsiang Wong Winston Wu Jingyu Yang Cheng-Yen Yeh Yau Yuen Yeung Chengjiu Yin Eric Yu Fu-Yun Yu Fei Yuan Alan Zemel Yuejun Zhang Zhenhong Zhang Yan Zhang Guoqing Zhao Nan Zhou

x

Organizing Committee Organizing Committee Chair Ronghuai Huang, Beijing Normal University, China Tutorials Chair Li Chen, Beijing Normal University, China Workshops Chair Tak-Wai Chan, Graduate Institute of Network Learning Technology, Taiwan Workshop Local Organization Chair Huanglingzi Liu, Beijing Normal University, China Doctor Student Consortium Chair Lora Aroyo, Eindhoven University of Technology, The Netherlands Exhibition Chair ShengQuan Yu, Beijing Normal University, China Organizing Committee Secretariat Zheng Chen, Beijing Normal University, China Yan Dong, Beijing Normal University, China Ping Li, Beijing Normal University, China Qian Li, Beijing Normal University, China Jiaoyang Guo, Beijing Normal University, China Jingbin Zhang, Beijing Normal University, China Lanqin Zheng, Beijing Normal University, China

Sponsors Ministry of Education, the People’s Republic of China Chinese Association for Artificial Intelligence

xi

Contents Preface Riichiro Mizoguchi, Pierre Dillenbourg and Zhiting Zhu Program Committee

Additional Reviewers

Organizing Committee

v vii ix

x

Keynote Speeches The Design of Effective Simulation-Based Inquiry Learning Environments Ton de Jong

E-Learning Evolution: From M-Learning to Educational Semantic Web and Beyond Cui Guangzuo, Yang Gongyi, Chen Hu, Fei Chen and Guo Jiuling Designed Collaboration as a Scaffold for Schematic Knowledge Integration Naomi Miyake

Technology Affordances for Intersubjective Meaning-Making Daniel Suthers

3 7 15 21

Modeling and Representation Design of an Environment for Developing Presentation Skills Kazuhisa Seta and Mitsuru Ikeda

Ontological Modeling Approach to Blending Theories for Instructional and Learning Design Yusuke Hayashi, Jacqueline Bourdeau and Riichiro Mizoguchi A Case of Blending Learning in Computer Teaching –– The Model and the Application Li Cuiling, Hong Wang and Huiyu Zhang A Combined Method for Extracting Rules with Improved Quality Fuyan Liu

29 37 45 49

Programming The Impact of CABLE on Teaching Computer Programming Ioana Chan Mow, Wing K. Au and Gregory C.R. Yates

Problem Solving Process Oriented Diagnosis in Logic Programming Nguyen-Thinh Le and Wolfgang Menzel

55 63

xii

Programming Teaching Support System Using Student Model KeunWoo Han, EunKyoung Lee and YoungJun Lee

A Method for Creating Teaching Materials of Practical Object-Oriented Methods Education Izuru Kume, Naoya Nitta and Yasuhiro Takemura

71 75

Science Education at School Science Net: Effects of an e-Learning System on Elementary School Students’ Self-Regulated Learning in Science Classes Takeshi Kitazawa, Masahiro Nagai, Hiroshi Kato and Kanji Akahori Using Satellite Resources for Scientific Inquiry Beaumie Kim, Manetta Calinger and Debbie Denise Reese

Experimental Researches on Development of Pupils’ Advanced Cognitions in PRIME Environments Zhou Yueliang, Lianghui Wang and Xiuqin Lin

81 89 97

ITS, et al. Teaching Chinese Handwriting by Automatic Feedback and Analysis for Incorrect Stroke Sequence and Stroke Production Errors Kai-Tai Tang and Howard Leung

Developing a Practical Domain Knowledge Base and Problem Solving System for Intelligent Educational System of High School Chemistry Nana Ishima, Toru Ueda, Tatsuhiro Konishi and Yukihiro Itoh

The COLAC Model: Collaborative Paper-Writing in the Humanities Guillaume Schiltz and Andreas Langlotz

Experimental Investigation and Implementation of Support for Problem Generation by Presenting Cases Kazuaki Kojima and Kazuhisa Miwa A Computer-Based Environment for Learning by Problem-Posing as Sentence-Integration Tsukasa Hirashima, Takuro Yokoyama, Masahiko Okamoto and Akira Takeuchi

107 115 119 123 127

Discussion The Impact of Structured Discussion on Students’ Attitudes and Dispositions Toward Argumentation Khai Seng Hong, Ole C. Brudvik and Yam San Chee

Assessing the Impact of a Structured Argumentation Board on the Quality of Students’ Argumentative Writing Skills Ole C. Brudvik, Khai Seng Hong, Yam San Chee and Libo Guo

133 141

xiii

Incorporating Online Discussion in Classroom Learning: A New Strategy Wenli Chen and Chee Kit Looi

Using Agents for Enhancing Learning Effects in an Advanced Discussion Forum Yuejun Zhang, Kinshuk, Øyvind Smestad, Jingyu Yang and Lynn Jeffery

149 157

Emotion & Personality Research on Personality Mining System in E-Learning by Using Improved Association Rules Luo Qi, Yanwen Wu, Liyong Wan and Ying Yu

Analysis on Relationships of Emotional Transmissions Between Participants and Their Emotional Aspects in Communication Using Bulletin Board System Shogo Kato, Yuuki Kato and Kanji Akahori

Learning Nonverbal Emotion Interaction in 3D Intelligent Virtual Environment for Children Zhen Liu Development of Know-How Information Sharing System in Care Planning Processes Kaoru Eto, Tatsunori Matsui and Yasuo Kabasawa

167 171 175 179

CSCL Student Learning and Team Formation in a Structured CSCL Environment Nobel Khandaker, Leen-Kiat Soh and Hong Jiang

An Integrated Framework for Fine-Grained Analysis and Design of Group Learning Activities Seiji Isotani and Riichiro Mizoguchi

The Development of a Grouping System in a Collaborative Learning Environment Pao-Ta Yu, Yen-Shou Lai, Chia-Ming Liu and Jenq-Muh Hsu

Students’ Understandings and Attitudes Toward Group Learning: An Empirical Study Jianhua Zhao and David McConnell

The Effectiveness of Knowledge Building Through Computer Supported Collaborative Learning Among Elementary Students: A Case Study Wing Cheung Alex Tse, Fong Lok Lee and Yong Ou

Computer-Supported Content Analysis for Collaborative Knowledge Building in CSCL Jian Liao, Yanyan Li, Ying Zhou, Ronghuai Huang and Jingjing Wang

A Novel Web-Based Collaborative Learning Supporting System with Navigation Function Tian Chenyuan, Zuoliang Chen and Shigayeshi Watanabe

185 193 201 205 209 217 225

xiv

Towards Auto-Coding of Collaborative Interaction Texts Based on Maximum Entropy Approach Jian Liao, Ronghuai Huang, Yanyan Li, Jingjing Wang and Jing Leng

Web Based Collaborative Environment for Engineering Graphics Education Lianguan Shen, Mujun Li, Xiaodong Wang, Wei Zhao and J.J. Zheng Time-Based Self-Learning Support Using Collaborative Learning Process Masahide Kakehi, Tomoko Kojiri and Toyohide Watanabe

The Design of a Collaborative Learning Environment in a Mobile Technology Supported Classroom: Concept of Fraction Equivalence Siu Cheung Kong

Scientific Modeling of Technology-Mediated Collaborative Learning Processes Yau-yuen Yeung Exploring the Learning Effect of a Web-Based Learning Community on EMBA Students I-Fan Liu, Meng Chang Chen and Yeali Sun Development of a Discussion Board System Designed for the Group Discussion That Includes Peer-Review Process Shigeru Sasaki and Hiroyoshi Watanabe

229 233 237 241 249 257 261

Interface Improving Creativity for Mathematical Problem Solving Using Web-Based Multimedia Whiteboard System Wu-Yuin Hwang, Nian-Shing Chen, Jian-Jie Dung and Yi-Lun Yang

Developing a VR-Based Projectile System Using Haptic Device for Learning Physics Atsushi Kanbe, Yukihiro Matsubara, Noriyuki Iwane and Kimiko Hirayama Proposal for Digital Partners Project Chris Davies and Jingjing Zhang

267 275 283

Assessment and Evaluation Development of Portfolio Assessment Support System Yasuhiko Morimoto, Isao Kikukawa, Maomi Ueno, Setsuo Yokoyama and Youzou Miyadera

Peer-Assessment in Web-Based ePortfolios System: An Experimental Study Youmei Wang Construction and Performance Evaluation of High Quality Curriculum Integrated with Information Technology YouRu Xie and Rui Yin

Effectiveness of WebQuest in the Teaching of STS in Secondary Biology Ka-leung Tse and Sai-wing Pun

289 297 305 309

xv

Mobile and Web-Based Learning Effects of Using Digital Contents Designed for PDA as a Teaching Aid in an Observational Learning of Planktons for Fieldworks on a Ship Hitoshi Miyata and Mitsuo Ishigami

A Study of Message Reading Efficiency of Color Screen Mobile Phones Xuemin Zhang, Bo Wang, Lilin Rao, Bin Yang, Yongna Li and Xueming Lu

The Design of a Web-Based Learning Platform: A Case Study in Taiwan I-Fan Liu, Meng Chang Chen and Yeali Sun

A Study of Implementing Web-Based Learning Systems to Enhance Learning for the Supply Chain Management (SCM) Course in Higher Education I-Fan Liu and Shelley S.-C. Young

What Is Expected of a Facilitator in a Virtual Learning Environment? Ni Chang

315 323 327 331 335

Social Networking & Blog Proactivity, Autonomy & Social Networking: Transitional Environments for the Japanese Educational Context Deborah C. Turk and John W. Brine

341

Effects of Peers Interactivity and Self-Regulated Learning Strategies on Learning Art Appreciation Through Weblog Sau Hung Cheung and Percy Lai Yin Kwok

349

Understanding Asynchronous Teaching and Learning Dialogues – An Integrative Approach E. Vass, F. Concannon, M. LeVoi, K. Littleton and D. Miell

357

An Application of Social Network Analysis in Evaluation of CSCL Yonggu Wang and Kedong Li

353

Cultural Issues Participatory Agent-Based Gaming Methodology in Cross-Cultural Education: Exploring Efficient and Sustainable Civil Society and Community Reiko Hishiyama and Toru Ishida Internet for Senior Citizens in China: Survey and Proposal Wei Zhou, Takami Yasuda and Shigeki Yokoi

Why Do Students Engage in e-Learning: A Chinese Perspective Zhenhong Zhang and Ronghuai Huang

Enabling a Multilateral Distance Class Between China, Korea and Japan: Effective Utilization of Networking Technologies Yuri Nishihori, Keizo Nagaoka, Nozomu Nishinaga, Kenji Tanaka, Yuichi Yamamoto, Haruhiko Sato, Masahiro Harada, Ruimin Shen, Jinjin Feng and Myunghee Ju Kang

363 371 379 383

xvi

Content and Knowledge Management Digital Video Database: Supporting Student Teachers’ Learning About Teaching During Teaching Practice Winnie So, Vincent Hung and Walker Yip

Building and Evaluation of a Semantic Web System That Provides Teachers with Lesson Plans Toshinobu Kasai, Haruhisa Yamaguchi, Kazuo Nagano and Riichiro Mizoguchi An Improved Learning Content Management System Framework Liyong Wan, Chengling Zhao, Ming Zhao, Luo Qi and Libing Jiang Resource Based Solution to Teachers’ Knowledge Management Sun Hongtao, Lu Wang and Hongwei Dai

Collaborative Building of Japanese Kanji Pronunciation Database for Learning Japanese by Chinese Fei Yuan, Jing Yuan, Rong Wang, Hiroyuki Mitsuhara, Kazuhide Kanenishi and Yoneo Yano

Using IT to Power and Support Problem-Based Engaged Learning Wee-Meng Hoe and Irene Tan

389 397 405 409 413 417

IT at School & Teacher Training A Study of Innovative Uses of ICT in Primary Education Shelley S.-C. Young and Hsin-Ho Ku

Impacts of Grade 7-9 Students’ Computer Usage After School on Academic Achievement: A School Case Study Yiu Fai Wong and Percy Lai Yin Kwok Using Interactive Whiteboards (IWB) to Enhance Learning and Teaching in Hong Kong Schools Fong-lok Lee, Sai-wing Pun, Sandy Siu-cheung Li, Siu-cheung Kong and Wai-hung Ip

Conditions Facilitating the Implementation of Information Communication Technology Integration in Malaysian Smart School Wan Zah Wan Ali, Hajar Mohd Nor, Azimi Hamzah and Nor Hayati Alwi A Study of the Present Status of IT Teachers Training in Microsoft’s ‘Partners in Learning’ Project –– A Content Analysis Approach GuiJing Huang, Yong Xu, JinBao Zhang and XiaoYuan Wang

inPD: An Emerging Theoretical Framework for Educational Professional Development in the Information Age Simon Hughes

423 431 439

443

447 451

xvii

Reflection and Self-Directed Learning Automated Mentoring for Reflection in an Eportfolio Tzemin Chung, Mun Kew Leong and Joel P.L. Loo

Guided Map for Scaffolding Navigation Planning as Meta-Cognitive Activity in Hyperspace Akihiro Kashihara, Mitsuyoshi Nakaya and Koichi Ota Self-Directed Learning in Technology Supported Project Work Allan H.K. Yuen and Liping Deng

Multi-Step Annotation to Promote Reflective Learning with a Mobile Phone N. Gotoda, K. Matsuura, K. Kanenishi, K. Niki and Y. Yano

457 465 473 477

Game and Edutainment Property Exchange Method for Automatic Generation of Computer-Based Learning Games Takanobu Umetsu, Tsukasa Hirashima and Akira Takeuchi Development and Validation of an Animation-Based Test in the Area of Earth Sciences Huang-Ching Wu and Chun-Yen Chang

Weaving Pedagogy into Gaming: Learning Design Principles for Developers Yam San Chee, Yi Liu and Khai Seng Hong VR Edutainment Material Interlude for Dynamics Experiment and the Development Platform Prelude Yuma Hanafusa, Takashi Inoue, Hiroyuki Tominaga, Toshihiro Hayashi and Toshinori Yamasaki Using the “Record-Replay” Function for Elaboration of Knowledge in Educational Games Junjie Shang, Morris S.Y. Jong, Fong-Lok Lee, Jimmy H.M. Lee, Marti K.H. Wong, Eric T.H. Luk and Kevin K.F. Cheung

483 491 495 499

503

Participation/Attitude Toward Learning Investigating Learner Autonomy Toward e-Learning Shu-Sheng Liaw and Hsiu-Mei Huang

Moderating Role of Online Self-Efficacy in Relation Between Learning Strategy and Online Performance Huamao Peng, Ying Wang and Ronghuai Huang

Understanding E-Learners’ Characteristics and Performance in Online Courses Rowena Santiago and Minoru Nakayama An Exploratory Study on Teachers’ Perceptions of Game-Based Situated Learning Morris S.Y. Jong, Junjie Shang, Fong-Lok Lee, Jimmy H.M. Lee and Huk-Yuen Law

509 517 521 525

xviii

Visualization Visualizing Errors for Self-Correcting Discrepancy Between Thinking and Writing Hidenobu Kunichika, Tsukasa Hirashima and Akira Takeuchi

Using Systematic Animation to Teach Dynamic Science Concepts Othman Talib, Christirani Azhar Shah and Nabila Abdullah

Creating Animations in SVG Format for Visualizing Program Execution Koji Kagawa

Effects of the Voice Recognition on the Writing of Students with Learning Disabilities Hu Lailin

535 543 551 555

Curriculum Designing a Teacher Professional Knowledge Base and Its Operation Model Based on School-Based Curriculum Development Yih-Ruey Juang, Tak-Wai Chan and Tzu-Chien Liu

561

Development of a Photo Management System in Schools Which Ensures Students Appear Equally Kyoko Umeda, Shinsuke Takito, Tetsuro Ejima and Hironari Nozaki

577

Dynamic Composition of Curriculum for Personalized E-Learning Yanyan Li and Ronghuai Huang

569

Sense-Making and Facilitation Probing Technology as Affordances for Negotiating Meaning in the Elementary Science Classroom –– A Participation Perspective Fei-Ching Chen, Huo-Ming Jiang, Jie-Chi Yang and Yu-Wei Lee

Analysis of Meaning Making in Online Learning Daniel Suthers, Nathan Dwyer, Richard Medina and Ravi Vatrapu

Facilitating Knowledge Construction by Providing Individualized Services Weidong Pan and Igor Hawryszkiewycz

The Role of On-Line Facilitators: Types of Collaborative Skills for Effective E-Learning Activities Tengku Putri Norishah Tengku Shariman and Habibah Abdul Jalil

587 595 603 611

PBL and Test Fostering Project-Based, Active Learning Through Use of Technology Teresa Bader and Teh-yuan Wan Framework for Problem-Solving Based Learning in Nursing Domain – A Practical Study – Yukie Majima, Yoichiro So and Kazuhisa Seta

621 625

xix

A Polytomous Computerized-Adaptive Testing That Rewards Partial Knowledge Yung-Chin Yen, Rong-Guey Ho and Li-Ju Chen

Research on Algorithm of Computer Adaptive Test Using Optimized MDPLTM Jin-Ling Li, Feng-lin Wang and Wang-Xiu Li

629 637

Doctor Student Consortium (DSC) Papers Cognitive Maps-Based Student Model Alejandro Peña, Humberto Sossa and Agustín Gutiérrez

643

Improving Quality of Online Forum Interactions in Distance Higher Education Zhenhong Zhang and Ronghuai Huang

645

Annotation in Information Research for Decision Making Robert Charles and David Amos

649

Applying Weighted Learning Object to Build Adaptive Course in E-Learning Anh Nguyen Viet and Dam Ho Si

Effects of the Use of Graphic Calculators on Cognitive and Metacognitive Domains in Teaching and Learning of Mathematics N. Mohd. Tajudin, R. Ahmad Tarmizi, W.Z. Wan Ali and M.M. Konting Improve Effectiveness of Dialogue in Learning Communities Jingyu Yang and Kinshuk

Validation of the Mathematics Courseware Usefulness Evaluation Instrument S.A. Noraidah, A.G. Abdul Azim, S. Hasan and M.Y. dan Aida Suraya Expert Tutoring and Natural Language Feedback in Intelligent Tutoring Systems Xin Lu Learning Environment for Designing Physics Experiment: DEEP Takahito Toumoto, Tomoya Horiguchi, Tsukasa Hirashima and Akira Takeuchi

647

651 653 655 657 659

Standards, Adaptation & Pedagogy: Quality Assessment in e-Learning Silvia Sanz-Santamaría, Julián Gutiérrez Serrano and José A. Vadillo Zorita

661

Author Index

663

This page intentionally left blank

Keynote Speeches

This page intentionally left blank

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

3

The Design of Effective Simulation-Based Inquiry Learning Environments Ton de Jong Faculty of Behaviorial Sciences, University of Twente, The Netherlands [email protected] Abstract: Modern theories on learning and instruction call attention to learning environments that create constructivistic, situated, and collaborative learning experiences. Simulations offer specific features that enable self-directed, highly autonomous, high interaction learning. First, learning in these environments differs from learning in expository environments in that it puts a higher emphasis on inquiry processes such as hypothesis generation and testing and on regulative processes such as planning and monitoring. Second, these environments offer specific opportunities to situate learning in realistic settings, but they also offer the possibility to adapt reality to support learning. Third, inquiry learning presents opportunities for negotiation and collaboration. This presentation will set out characteristics of simulations discuss characteristic inquiry processes and associated problems, and examine what is needed to design effective inquiry learning environments.

Introduction New types of (on-line) learning environments are becoming available for use in the actual classroom rapidly. Development that nowadays dominate the field of learning and instruction are constructivism, situationism, and collaborative learning. More specifically, we can say that the new view on learning entails that students are encouraged to be active in construcingt their own knowledge, in realistic situations, together with others. Technology plays a major role in implementing these new developments in education. Constructivism is supported by computer environments such as hypertexts, concept mapping, simulation, and modeling tools (see de Jong & van Joolingen, 1998), realistic situations can be brought into the classroom by means of for example video or even virtual reality, and collaborative learning is supported in Internet based learning environments such as Co-Lab (van Joolingen, de Jong, Lazonder, Savelsbergh, & Manlove, 2005). In this presentation I will focus on the riole of computer simulations in relation to inquiry learning.

1. Characteristics of computer simulations Computer simulations are programs that hold a computable model of some kind of reality. Students can manipulate the simulated reality by changing values of input variables, and the output is usually displayed in several formats (e.g., animations, graphical displays, and numbers). These characteristics make simulations well suited for implementing the trends above. First, simulations elicit a learning process that is called inquiry learning. In inquiry learning, a domain is not directly offered to students; rather students have to induce characteristics of the domain from experiences or examples. Inquiry learning can be defined as an approach to learning that involves a process of exploration, that leads to asking

4

T. de Jong / The Design of Effective Simulation-Based Inquiry Learning Environments

questions and making discoveries in the search for new understandings (National Science Foundation, 2000). This is a learning approach that is in line with contructivistic principles. Second, simulations can easily be used to introduce realities in the classroom; interfaces of simulations may mimic any reality. Third, inquiry learning is very appropriate for collaborative learning, as is real, scientific, inquiry since it requires students to make decisions along the way (e.g., which hypothesis to test) that are good anchor points for knowledge exchange and negotiation (Gijlers & de Jong, 2005).

2. Inquiry learning Though simulations seem to be able to take a central role in realizing the above mentioned trends in education, students do have considerable trouble in realizing an effective inquiry process. Knowledge about the inquiry process and the related problems students experience may help to design adequate cognitive scaffolds.

2.1 Inquiry learning processes Although there may be some variations, for example in the way data are gathered (e.g., from experimentation or from data sets), and variations in the complexity of the experimentation, there is a fair consensus about which processes basically comprise inquiry learning. The different classifications in the literature differ mainly in their granularity, ranging from very detailed to rather broad, but basically do not differ in the processes that are distinguished. In de Jong (2006b), I introduced a set of learning processes that form a suitable basis for describing inquiry learning: In orientation, the student makes a broad analysis of the domain; in hypothesis generation, a specific statement (or a set of statements, for example, in the form of a model) about the domain is chosen for consideration; in experimentation, a test to investigate the validity of this hypothesis or model is designed and performed, predictions are made and outcomes of the experiments are interpreted; in conclusion, a conclusion about the validity of the hypothesis is drawn or new ideas are formed; and, finally, in evaluation, a reflection on the learning process and the domain knowledge acquired is made.

2.2 Problems that students experience In a review of research, de Jong and van Joolingen (1998) concluded that students may have serious problems with all of the above mentioned inquiry learning processes. In general, students may have trouble stating hypotheses, designing experiments, and interpreting data; they often do not engage in overall planning and do not adequately monitor what they have been doing (de Jong & van Joolingen, 1998). These inquiry process problems may be associated with wrong mental models of systems in general (Kanari & Millar, 2004; Kuhn, Black, Keselman, & Kaplan, 2000) and it may lead to a misinterpretation of experimental outcomes from the experiments that were performed in the inquiry (Chinn & Brewer, 1993). On this basis and also based on overall research in inquiry learning many researchers, therefore, conclude that students need guidance in the discovery process (de Jong, 2006a; Mayer, 2004).

T. de Jong / The Design of Effective Simulation-Based Inquiry Learning Environments

5

2.3 Providing students with cognitive scaffolds In the software students can be supported with cognitive tools or cognitive scaffolds to ensure an effective inquiry learning process. An example of this is providing students with assignments. These assignments help to students in their planning activities and they help to focus on relevant aspects of the simulation (van Joolingen & de Jong, 2003). Another example is a monitoring tool. A monitoring tool helps students to save all their experiments, to re-order, and to replay them. A hypothesis scratchpad offers students elements (variables, relations, conditions) for composing hypotheses. Students can also be provided with hints on how to experiment, or on how to reflect over the knowledge that is acquired. Another way to support students is to offer them just-time background information or explanations. Finally, the inquiry process can be subdivide in several phases and for every phase students can be offered a specific structure to work in. Extensive overviews of cognitive tools or cognitive scaffolds can be found in Quintana et al. (2004) and de Jong (2006b).

3. Conclusion Large scale environments show that inquiry learning based on simulations can be an effective learning process (Hickey & Zuiker, 2003; Ketelhut, Dede, Clarke, & Nelson, 2006; White & Frederiksen, 1998) . However, the inquiry process needs to be scaffolded to reach these results. This scaffolding can be offered in the software, but could also be offered by the teacher or a co-learner. Combined with more realistic interfaces simulations can indeed offer educational opportunities that combine contructivist, collaborative, and situational characteristics. For sure, to give inquiry learning a place in the curriculum a balance needs to be found between inquiry learning and other ways of learning and instruction so that an integrated, attractive, and effective curriculum results.

References Chinn, C. A., & Brewer, W. F. (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research, 63, 1-51. Cognition and Technology Group at Vanderbilt. (1997). The Jasper project; Lessons in curriculum, instruction, assessment, and professional development. Hillsdale (NJ): Lawrence Erlbaum Associates. de Jong, T. (2006a). Computer simulations - Technological advances in inquiry learning. Science, 312, 532-533. de Jong, T. (2006b). Scaffolds for computer simulation based scientific discovery learning. In J. Elen & R. E. Clark (Eds.), Dealing with complexity in learning environments (pp. 107-128). London: Elsevier Science Publishers. de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68, 179-202. Gijlers, H., & de Jong, T. (2005). The relation between prior knowledge and students’ collaborative discovery learning processes. Journal of Research in Science Teaching, 42, 264-282. Hickey, D. T., & Zuiker, S. (2003). A new perspective for evaluating innovative science learning environments. Science Education, 87, 539-563. Kanari, Z., & Millar, R. (2004). Reasoning from data: How students collect and interpret data in science investigations. Journal of Research in Science Teaching, 41, 748-769. Ketelhut, D. J., Dede, C., Clarke, J., & Nelson, B. (2006). A multi-user virtual environment for building higher order inquiry skills in science. Paper presented at the American Educational Research Association, San Francisco. Kuhn, D., Black, J., Keselman, A., & Kaplan, D. (2000). The development of cognitive skills to support inquiry learning. Cognition and Instruction, 18, 495-523. Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning? American Psychologist, 59, 14-19.

6

T. de Jong / The Design of Effective Simulation-Based Inquiry Learning Environments

National Science Foundation. (2000). An introduction to inquiry. In Foundations. Inquiry: Thoughts, views and strategies for the K-5 classroom. (Vol. 2, pp. 1-5). Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R. G., et al. (2004). A scaffolding design framework for software to support science inquiry. The Journal of the Learning Sciences, 13, 337-387. van Joolingen, W. R., & de Jong, T. (2003). SimQuest: Authoring educational simulations. In T. Murray, S. Blessing & S. Ainsworth (Eds.), Authoring tools for advanced technology educational software: Toward cost-effective production of adaptive, interactive, and intelligent educational software (pp. 1-31). Dordrecht: Kluwer Academic Publishers. van Joolingen, W. R., de Jong, T., Lazonder, A. W., Savelsbergh, E., & Manlove, S. (2005). Co-Lab: Research and development of an on-line learning environment for collaborative scientific discovery learning. Computers in Human Behavior, 21, 671-688. Vreman-de Olde, C., & de Jong, T. (2006). Scaffolding the design of assignments for a computer simulation. Journal of Computer Assisted Learning, 22, 63-74. White, B. Y., & Frederiksen, J. (1998). Inquiry, modelling, and metacognition: making science accessible to all students. Cognition and Instruction, 16, 3-118.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

7

E-learning Evolution: From M-learning to Educational Semantic Web and Beyond Cui Guangzuo, Yang Gongyi, Chen Hu, Chen Fei, Guo Jiuling Modern Education Technology Center, Peking University, China [email protected] Abstract: An e-learning model with ICT technology (ELM) is proposed in this paper. With this model, some education technologies and e-learning evolution are interpreted, such as network education, mobile education, ubiquitous education, educational semantic web, and etc. In the meantime, the way of how to combine new ICT technologies into education is also demonstrated. After discussing the convenience and challenge of various education technologies, a new model called intelligent education is introduced and some recent research results are presented. At last, the author looks ahead the future of information technology and human related disciplines and their effects on education. Keywords: E-learning, M-learning, Educational Semantic Web, Intelligent Education

1. E-learning Model with ICT Technology E-learning means the education technology enhanced by ICT technology. With the development of new ICT technologies, e-learning has been improved continuously, and as a result, new education technologies emerged and named after the corresponding ICT technology, such as network education, mobile education, ubiquitous education and educational semantic web. What will the ICT technology tend to and what will be the next education technology? To deal with this, a new model for e-learning technology is proposed, depicted as figure1. In ELM model, ICT technology enhances education in the following way: information technology is used to design and present course content, and communication Figure1 E-learning Technology Model technology is used as an interaction method among educate, Educatee and educational agent. With the information technology’s evolution from data model to information model and now to knowledge representation, and communication technology’s evolution from wire to wireless and now to mobile communication, the e-learning technology evolutes from CAI, network education, mobile education, ubiquitous education to educational semantic web and so on. In the following, these e-learning technologies are demonstrated.

8

C. Guangzuo et al. / E-Learning Evolution: From M-Learning to Educational Semantic Web

2. From M-learning to Educational Semantic Web Network Education: with fixed wired network and information model. Mobile Education (also named m-learning) [1-6]: Courseware is designed with data model and information model; communication with mobile network. So, the interaction can be completed anywhere and anytime with text message. On the other hand, the courseware can not be accessed by some devices with different contexts, such as different screen size. So, in m-learning, not all the devices can access the published course content. Ubiquitous Education (also called u-learning): is almost the same as m-learning except Figure2 Portal for PC Screen that in u-learning universal information access (UIA) is required [19]. UIA means that the published courseware can be accessed anywhere, anytime, any network and by any device. UIA has been one unresolved key problem in ubiquitous computing which is also called device independence. The related factors which affect UIA include input device, output device, network type, device screen, OS type, application tool, human preference, dynamic environment, unpredictable action, and etc. To demonstrate the difficulty of UIA, we give an explanation. Figure2 is an education portal designed for PC which looks very well with PC screen. But what about is it when accessed with PDA or Hand Hold? It is terrible. So what kind of resource should course content be for UIA? Let’s look at an example of figure3.

Figure3 Adaptation of SCORM Resource

Figure3 represents a part of courseware resource with SCORM standard. What to present when a device accesses this course at first time? The well solution is that the content of first screen should depend on, at least, the screen size. Figure4 Adaptation with Resource Tree That is, with different screen size, the presented content should match with that size. The above problem is called content adaptation. How to implement content adaptation? This can be demonstrated in figure4 [7]. From figure4, we can see that with resource tree representation, the proper content size can be selected according to the screen size. But the candidate resource sets maybe more than one, which node set should be selected? At the worst case, the selected content may be has none meaning. So, to produce the proper content, other constraints should be

C. Guangzuo et al. / E-Learning Evolution: From M-Learning to Educational Semantic Web

9

confined, that is, the selected resource node set should be a meaningful block in logical. How to guarantee that? It should refer to educational semantic web. Educational Semantic Web: Educational Semantic Web (ESW) [9-12] is a new education technology enabled by semantic web technology [8]. In ESW, the education content and activity should be annotated with well-defined meaning which is understandable to human and machine. The well-defined meaning is represented with a special data model, called education ontology [14-16]. In education ontology, the concepts and their relations in course content and education activity are defined in a formal method called description logics. With these logical defined educational resources, intelligent education application can be developed by reasoning with logic programming and rules. The educational semantic web diagram is depicted as figure5 and a draft educational ontology is as figure6 (proposed by author’s group).

3. Intelligent Education With the research and development of ICT, new technologies will come out, and where should education technology go? From the above statement, we can see that e-learning technologies are developed for education practice, that is, only for educate and educatee. It is natural to think that ICT technology should not be confined only for education practice. The problem is: how does ICT technology provide support to education research (theory and method)? Is it possible? And a more challenge problem is: are there any other technologies or theory, except for ICT, that can be used to enhance education? Of course, these are human related disciplines, such as brain science, cognition science, knowledge science, logics, philosophy, and etc. The evolution of education technology with human related disciplines and ICT technology is depicted as figure7. In figure7, D-Com stands for data communication, D-Process stands for data process and K-reasoning stands for knowledge reasoning. From figure7, we see that the

10

C. Guangzuo et al. / E-Learning Evolution: From M-Learning to Educational Semantic Web

development trend of information technology is knowledge representation and reasoning (KRR). With KRR, intelligent applications can be developed. On the other hand, from the viewpoint of application, domain users can also model and develop applications without relying on software engineers. In other words, education domain users include educate, educate, researcher, and other ones who are engaged in education. So, as the development of technology, not only education practice, but also education theory can also be enhanced by ICT in an intelligent way. At the meantime, evolution roadmap of ICT is very like the cognition process for human being to learn knowledge, depicted as figure8. As figure8 indicates, the cognitive process is as follows: collect large amount of data; select useful data (information) from large data; extract knowledge about a concept from synthesizing multiple aspects of information and setting up connection with other concepts; the knowledge is kept in long-term store. At the meantime, data selection and information synthesis are controlled by thinking and reasoning with knowledge originated from motivation. On the other hand, with the information model’s evolution from data model, information model to knowledge model, the course model changes from digital course, multimedia course to knowledge representation course. All of these reflect that the development process of information technology is almost consistent with the cognition process. Figure7 also indicates that, besides ICT, human related disciplines are also an important force to improve education. From viewpoint of history of thousands of years, human discipline plays an important role to education. Especially, in recent years, with the development of human related disciplines, we understand human mind more and more deeply, such as how to understand, how to learn, how to think, and etc. As a result, the human related achievements provide human a better way to understand themselves, to guide human to learn and to educate.

Figure9 Intelligent Education Architecture

In a word, with the rapid development ICT technology and human related disciplines, education will be enhanced in an intelligent way! Figure9 demonstrates more details of this intelligent way which called Intelligent Education Architecture (IEA) [17]. Figure9 indicates that IEA include five parts, they are Human Related Disciplines (HRD), Intelligent ICT (IICT, include Information Science, AI, Knowledge Engineering), Education Theory Research (ETR, includes Education Method), Education Practice and Intelligent Education Domain Engineering. Relations among these five parts are as follows:

C. Guangzuo et al. / E-Learning Evolution: From M-Learning to Educational Semantic Web

11

(1) Education Theory Research is supported by Human Related Disciplines, Intelligent ICT and Intelligent Education Domain Engineering. (2) Intelligent Education Domain Engineering is developed with the guide of Education Theory Research and Intelligent ICT. (3) Optimized Education Practice can make teaching process more efficient and the Human Related Disciplines can progress more rapidly. (4) All education activities are supported by Intelligent Education Domain Engineering. In the proposed Intelligent Education, all parts of education will form an ecosystem where every part in education will be enhanced by others. More details refer to [17].

4. Some Researches in Knowledge Science and Intelligent Education Laboratory at Peking University Knowledge Science and Intelligent Education Laboratory at Peking University (KSIE) aim to build an education ecosystem with Intelligent Education Architecture [17]. Such a system will provide scientific education research method and scientific education practice˄includes teaching and learning˅. With this system, the efficiency of education theory and method will be predicted by intelligent simulation or formal verification, not like the nowadays experiment with real person. The goal is good, but implementing such a system depends on a great progress of Human Related Disciplines and Intelligent ICT where the learning principle is understood and the proper model of learning nature is constructed. Indeed, it needs a lot of work and a long time, but we are doing something to proceed to the goal. Some researches on this work in recent years are presented as below.

4.1 Education Software Automation based on Education Ontology This means that, with educational ontology, we can develop an infrastructure platform to generate application system in terms of user requirements. Such an infrastructure is proposed as figure10, more details refer to [17].

4.2 Educational Semantic Web Service Composition Model Semantic Web Service (SWS) technology [13, 22, 23, 24] provides a good choice to realize education software automation. With SWS, every education service is annotated with well-defined meaning which can be understood by

12

C. Guangzuo et al. / E-Learning Evolution: From M-Learning to Educational Semantic Web

human and machine [18, 21]. With large amount of educational semantic web services available on www, we can compose some of them according to requirements and generate a realistic system. The above process is called SWS composition [18, 21, 25]. Figure11 is a proposed SWS composition model for education by our group which is called OntoComposer [21]. Figure12 is an implementation and its operation interface of OntoComposer, the labeled numbers in figure12 represent the process to compose a new service with available web services, details refers to [26].

4.3 Ontology-based Search Engine Search engine is an efficient tool to retrieve information from large amount of web resources. With semantic web, web resources are annotated with well-defined meaning understandable to human and machine. From figure5, with educational semantic web, educational ontology provides formal semantic description for educational resources which can be understood by machine. In this way, search engine can understand web resource and select information more efficiently (depicted as figure13), more details refer to [20].

4.4 Ontology-based M-learning Platform One problem in m-learning is context aware application. That is, the presentation of courseware content should be adaptive in terms of the user context. The user context includes device feature and user preference. When user accesses education server, the server selects proper content and style, and returns it to client. In this way, the courseware could be accessed by any device. Figure14 is a proposed design for m-learning with web service. The context-aware adaptation works as following: (1) at sever end, the e-learning

C. Guangzuo et al. / E-Learning Evolution: From M-Learning to Educational Semantic Web

13

system is composed of simple services which include operation and resource individually. (2) When server receives access request, it groups some simple services into one page in terms of user context automatically and returns it to client. The adaptive presentations are depicted as figure15. More details refer to [6].

5. Conclusions The advance of ICT would enhance education continuously. E-learning evolution with ICT indicates that M-learning provides a convenient interaction, educational semantic web provides a machine understandable description of education resource, and ontology model provides a knowledge representation model of course content, which will improve cognitive process for learn content in nature. Besides ICT, human related disciplines have become an important force to enhance education recently. A kind of education technology to combine ICT and human related disciplines into education and form a scientific ecosystem education system will be the next generation technology, which is called Intelligent Education.

References [1] Cui Guangzuo, etc. Mobile Education: A New Direction of Education Technology. Ceta Annual Conference of China. 2001.12. [2] Cui Guangzuo, etc. Mobile Virtual Campus: Design and Implementation. China Distance Education. 2002.12. [3] Cui Guangzuo, etc. Study and Implementation of SMS-based Mobile Education Architecture. GCCCE2002, Beijing. [4] Cui Guangzuo, etc. MVClass: Mobile Virtual Class for Open and Distance Education.International Conference of Distance Education " 2nd AEARU Workshop on Network Education”, Taiwan.2003.12 [5] Dong Sheqin, Yue Weining, Cui Guangzuo And Wang Guoping. A Educational Information Platform based on Mobile Device. Computer Application of Chine. 2004.V24.11, Pp142-145. [6] Cui Guangzuo, etc. A Study of Concept and Key Technologies of Mobile Education. China Distance Education. 2005.9. [7] Cui Guangzuo. WebUnify: An Ontology-based Web Site Organization and Publication Platform for Device Adaptation. SNPD2004 International Conference, Beijing. 2004.7. [8] Tim Berners-Lee, James Hendler and Ora Lassila. The Semantic Web. Scientific American 2001.5. [9] Lora Aroyo, Darina Dicheva. The New Challenges for E-learning: The Educational Semantic Web. IEEE Journal of Educational Technology & Society,2004.10.Pages 59-69. [10] Emanuela Moreale, Maria Vargas-Vera. Semantic Services in e-Learning: an Argumentation Case Study. IEEE Journal of Educational Technology & Society,2004.10.Pages 112-128. [11] Terry Anderson and Denise Whitelock The Educational Semantic Web: Visioning and Practicing the Future of Education. Journal of Interactive Media in Education, UK, 2004 (1). [12] Kendall Clark, Bijan Parsia and Jim Hendler Will the Semantic Web Change Education? Journal of Interactive Media in Education, UK, 2004 (1). [13] Sheila A. McIlraith, Tran Cao Son, and Honglei Zeng. Semantic Web Services. IEEE Intelligent Systems, MARCH/APRIL 2001. Pp46-53. [14] Cui Guangzuo. OntoEdu: Ontology based Education Grid System for e-learning. Fifth International Agricultural Ontology Service (AOS) Workshop, Invited report.2004.5. [15] Cui Guangzuo, etc. OntoEdu: Ontology based Education Grid System for e-learning. GCCCE2004,HongKong

14

C. Guangzuo et al. / E-Learning Evolution: From M-Learning to Educational Semantic Web

[16] Cui Guangzuo, etc.OntoEdu: A Case Study of Ontology-based Education Grid System for e-Learning . The Official Journal of Global Chinese Society FOR Computers in Education,2004,pp59-72 [17] Cui Guangzuo, etc. Architecture Study of Intelligent Education. Keynote Speaker. GCCCE2006, Beijing. [18] Yang Li-na. A Kind of Task Composition Method Based on Education Ontology. Master Thesis, Peking University, 2004.7. [19] Cui Guangzuo. Application and Challenge of education under Ubiquitous Environment. Ubiquitous Forum of Information Industry Ministry in China. 204.11. [20] Xiao Hong. Study on the Semantic of SCORM based on Knowledge Organization & Retrieval. Master Thesis, Peking University, 2006.7. [21] Liu Yang. A Kind of Ontology based Hybrid Semantic Web Service Composition: Mechinsm and Implementation. Master Thesis, Peking University. 2006.7. [22] Sheila A. McIlraith, Tran Cao Son, and Honglei Zeng,Semantic Web Services. IEEE Intelligent Systems.MARCH/APRIL 2001.pp46-54. [23] Sheila A. McIlraith,David L. Martin. Bringing Semantics to Web Services. IEEE Intelligent System,JANUARY/FEBRUARY 2003.pp90-94. [24] Massimo Paolucci.Autonomous Semantic Web Services. IEEE Intelligent Computing. SEPTEMBER • OCTOBER 2003. pp34-42. [25] Brahim Medjahed, Athman Bouguettaya, A Multilevel Composability Model for Semantic Web Services. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 17, NO. 7, JULY 2005.pp954-969. [26] Cui Guangzuo. OntoComposer: Development Model and Implementation of Semantic Web Service based on Domain Ontology. To be published.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

15

Designed Collaboration as a Scaffold for Schematic Knowledge Integration Naomi Miyake School of Information System and Technology, Chukyo University, Toyota, JAPAN [email protected] Abstract: Rapid change in modern society requires higher levels of learning such as the acquisition of adaptive or “schematic” knowledge. Rather than the efficiency of simply applying what one has learned, the schematic knowledge acquisition emphasizes portability, sustainability, and dependability of learning outcomes. Schematic knowledge is expected to allow the learners to apply them to solve the wider scope of similar problems, as well as to identify new problems and create new solutions. We have been developing and testing college level learning environments to enhance the acquisition of such schematic knowledge in the domain of cognitive science, by heavily relying on understandings of how and why collaborative reflection benefits learning. In the two-year curricular we have developed, the students are first introduced to the notion of schematic learning by experiencing their own formation of schemata, and then are guided to reflect upon the process, through carefully designed collaborative activities. They will also be encouraged to integrate their experiences to technical literature through collaborative discussion in a dynamically arranged jigsaw variations. I will report on the theoretical bases of our practice, concrete learning activities, technological supports, and some results of the evaluative analyses of the learning processes and the outcomes. Keywords: College level collaborative learning, schema formation, knowledge integration

Introduction: Emerging new learning goals Studies of how people could effectively learn have a long tradition because human beings have constantly been required to re-structure old experiences to accommodate new situations. However, in the current age, where rapid changes are the norm, the degree to which this requirement occurs has increased more than ever. Students, for example, are required not only to acquire routine knowledge but also to apply what they learn to new situations, outside of school and in a distant future. The outcomes are expected to be ‘portable’ so that the learners can bring them to apply problems outside of classrooms; the learning outcomes are also expected to be ‘sustainable’ so that the learners can build on them to keep strengthening their intellectual skills; the outcomes also have to be ‘dependable’ in the sense they are usable whenever and wherever need arises. To meet such requirements, new learning studies with new research methods have been carried out, which differ from those used in laboratory-based, conventional learning studies [1][2] , starting to yield some substantial reports [3][4].

1. Theories behind collaborative learning One of the major changes introduced by learning sciences is a change in the perspective on learning: learning sciences view learning as a collaborative act, where people mutually

16

N. Miyake / Designed Collaboration as a Scaffold for Schematic Knowledge Integration

enhance performance by influencing each other. There are two reasons for this change. One is that through many studies, collaboration has been proven to be effective in enhancing learning outcomes, in many disciplines and in different age groups [5][6][7][8][9][10][11]. The other reason is that collaborative situations help promote in-depth, process-oriented research in learning. The current rapid development of information recording technologies is playing a large role in research, by increasing the likelihood of learning studies achieving their potential of bringing significant change to actual education and progressing our understanding of the learning processes themselves. It has been widely acknowledged that a collaborative situation promotes motivation for comprehension [12]. Collaboration has also been a topic of research to see whether “Two heads are better than one,” and if so, how. It has also been repeatedly demonstrated that collaborative learning leads to adaptive acquisition of knowledge [13]. In order to understand the underlying mechanism for such results, some research has focused on the dynamic interaction between individual courses of understanding and their collaboration, while keeping the basic unit of analyses on the pair, by using tasks like jointly comprehending how a sawing machine saws [8] or what would be the area of a square sheet of paper to get the three-forth of its two-thirds, or the two-thirds of its three-forth [10]. On the sewing machine problem, even knowledgeable pairs could spend three to four hours easily to understand the hidden mechanism of how the internal bobbin works, through which each participant eventually constructed his or her own understanding, solidly different from the other partners’ comprehension. The detailed protocol analyses of such interaction also demonstrated that when caught in the “cal-de-sack” it was often the person who was monitoring the situation, not the person who was taking the initiative of the problem solving at the moment, who came up with an idea to radically change the situation to “save” them. These findings suggest that even during highly collaborative comprehension activities, social sharing of the situation does not impede each participant from pursuing individualistic knowledge construction. Rather, the interactive process supports each to realize different perspectives to check and modify their own understandings by making explicit the different perspectives, which are not within their individual repertoire [8]. On the square sheet area problems, paired participants, not the solos, could take alternating perspective-shifts from the local, task oriented view to more abstract perspective, and this alternation of the perspectives helped each participant to explicitly talk about their solutions in more abstracted forms toward the latter part of the interaction, thus enhancing the formation of abstracted solution [10]. This mechanism offers explanation why there could be different learning outcomes in each of the paired learners. It is because the abstraction level for each participant depends on the degree of the integration of the shared task-doing and the monitoring, during and at the end of the interaction. This also explains why a more knowledgeable participant, or the person who preceded the other in his/her understanding, could still benefit from the interaction. This model suggests that for the design of collaborative learning environments it is important to encourage role exchange and to secure ample chances for each individual learner to reflect upon the shared resources. 2. Designed Collaborative Reflection for the schematic knowledge integration In the project to test the theory described above at real classroom situations we have been developing a collaborative undergraduate curriculum to teach cognitive science for nearly 7 years [14][15][16][17][18][19][20][21]. The learning objective is broad because we believe knowledge about cognitive science has pragmatic value for most of what we do in our everyday lives. Cognitive science explains how human beings solve problems, make judgments, memorize events and schematize them, and create new ideas. Knowing how

N. Miyake / Designed Collaboration as a Scaffold for Schematic Knowledge Integration

17

people engage in such cognitive processes, with the associated strengths and weaknesses, helps in the development of a reflective, metacognitive viewpoint, which can be utilized in everyday practices of cognition. 2.1. Overall description of our curriculum and classroom activities The curriculum described here is for undergraduate students and covers two semesters per year, taking four years to complete. In the first year, hands-on experiences of simple cognitive tasks are completed and analyzed by the students, first individually and then collectively, in the class. They are then guided to reflect of their experiences to take metacognitive perspectives, to understand how people solve problems. The curriculum toward the end of the first year encourages them to tie their reflection to what is written in the textbooks, to integrate their personal knowledge to the theories of human problem solving. During their second year, these experience-based techniques are gradually meshed into more active reading of technical materials, to help students gain a deeper level of comprehension as well as to grasp the breadth of research in cognitive science. In the third to fourth years they are encouraged to engage in more inquiry-oriented, project-based learning, learning to do their research toward graduation theses. 2.2. Variations of the jigsaw method Throughout the curriculum, we use the various forms of the jigsaw method to enhance collaboration. A social psychologist, E. Aronson, devised this method in order to facilitate cultural merging in classrooms [22]. In his original design, a text may be divided into six parts each of which is read by a group of members, each of whom is responsible for different parts(this is called an “expert” group activity, because each is expected to be an expert of this assigned part). Then one member from each of those six groups gets together, for a group of six, to answer questions covering all six parts,requiring their equal participation to succeed(this is called the “jigsaw” group activity). The method produces a natural setting to explain what one understands to others, often motivating students to further examine their assigned parts. In our curriculum, students are introduced to a simple jigsaw having only two to three parts, gradually guided on to a more structured and dynamic jigsaw that covers twenty to thirty texts explaining different research findings. The jigsaw method is highly flexible, modifiable to facilitate many different types of collaboration. Suppose a complex and highly structured course work needs to be delivered among a class. One form we have developed in our curricula is a “matrix” jigsaw, wherein college students move in and out of hierarchically structured “expert” groups and “jigsaw” groups. Suppose you could prepare the learning materials in an n by m matrix, of perspectives A and B. Assign a student, say S1, to a cell Ai by Bj in the matrix. By doing this, S1 automatically becomes responsible of owing a particular viewpoint Ai on dimension A, as well as of another viewpoint Bj on dimension B. The course work can then be structured to require her to work in one of the expert groups on perspective A at one time, while at other times she is required to be an expert on B.  A more dynamically expansive Jigsaw has been devised in our course to enhance collaborative understanding of 20 to 30 learning materials, each representing classic research in three different domains of e-learning research. We expect the students to collaboratively read, explain, exchange, and discuss the materials to integrate them, to the level where each participating student would come out of the course with the ability of verbally explain what the entire field is, with many evidential pieces of knowledge they could cite by identifying relevant research[18][19][20].

18

N. Miyake / Designed Collaboration as a Scaffold for Schematic Knowledge Integration

This dynamic jigsaw models common activities of real world researchers. Professional researchers take different responsibilities to study a common theme and exchange their interpretations of existing work and new findings from their own community. They most often do this repeatedly, explaining their thoughts to different audiences so that they can examine them from different perspectives for different integration possibilities. This series of activities help them achieve a coherent comprehension, which could dynamically evolve over time. The dynamic jigsaw models this process and requires each participating student to first become an expert of his/her core material(s), then repeatedly explain the core to different audiences. While they receive information from others, they are also required to expand their explanations to include the new, and relate them to their own older knowledge structure. They are then to integrate these explanations to form a new explanation, to improve understanding. The dynamic jigsaw class replicates this more systematically. In the first phase, a student becomes an expert of one piece of the learning materials. S/he exchanges its explanation with one other student, to form an explanation of two pieces of the materials. Then, they are ready to exchange their two-piece explanations with a neighboring pair, who also is ready to give her two-piece worth. At the end of this phase, each student is expected to know four pieces, two of his/her own plus two more explained by another student. They can now exchange four to four, to cover eight research pieces, enough to cover a sub-domain. Toward the end of the term, such domain experts share by exchanging explanations of the domains, or some eight piece worth of information. 2.3. A concept mapping tool to support collaborative reflection Note-sharing software in the form of a concept-mapping tool could aid the students to express their ideas, integrate them among themselves as well as with others. This kind of technological support works well with the course structure, to promote the students knowledge integration [14]. Students are encouraged to externalize their initial ideas at early stages of their understandings of the learning materials, because it becomes easier to “talk about” or “collaboratively reflect on” externalized forms than to do so on their “ideas”

N. Miyake / Designed Collaboration as a Scaffold for Schematic Knowledge Integration

19

in the head. It is also easy to expand he initial concept maps by adding new cards, by connecting once completed map with other maps. The concept mapping tool we devised is called ReCoNote (Reflective Collaboration Note), some of its displays during the course is shown in Fig. 1. We have also devised an evaluation tool to follow their development. The right column on Fig. 1 shows one student’s three concept maps drawn at different stages of a course in year 2004, put together to display the entire map so that both the students and the teaching team can 3. Major findings Some assessment analyses of their performance accumulated over the last 6 years reveal 1) fair amount of retention of the learning materials 4 to 6 months after the end of the course, 2) explicit knowledge integration surrounding each students’ personal needs, and 3) some conscious learning of learning skills such as asking specific, content-driven questions [15][16][18][19][20]. One index we have devised is the degree of integration, or structural coherence, of the final concept maps the students created after the course, before the deadline of the term papers. The maps were categorized into four groups according to their structural coherence and given scores ranging from 1 to 4, with 4 indicating the highest coherence. The term papers are also evaluated in terms of the conciseness and the correctness of the descriptions of the research findings they reported. We also examine the relationship between the implications they drew from what they had learned and their descriptions of possible usage in everyday life. We called this measure “extendibility.” We found that the integrity measures of the concept maps were generally high. Forty-two percent of the final concept maps were categorized as achieving high integrity, close to the performance of novice graduate students. In classes in recent years, all the learning materials were covered in 80 to 85% of the term papers, out of which some 50 to 60% being “concise descriptions” with the necessary components in an expected order. This indicated that the majority of the students had learned both the basic contents of the learning materials as well as how to give concise summaries. The extendibility measure, or the degree to which the students could connect what they learned to their daily experiences, was found to be positively correlated with the quality of concept maps, suggesting that the learning activity of externalizing their integration efforts had a positive effect of fostering their thinking toward applications of what they had learned [18]. The protocol analysis of the students’ conversations during the class revealed that the students’ explanations became more concise in terms of both the amount of time used and the content covered. To take three students as a representative example: their first explanation of one research paper took 400 to 500 utterances on average, which decreased to 20 to 30 utterances toward the end of the term, without losing any necessary components. Their first explanation attempts were closer in wording to the texts of the learning materials than their later explanations, yet the first explanations involved more incorrect, vague, or confused statements. Such confusions about the meanings of the learning materials tended to be resolved during the discussions that occurred while the students were integrating their materials with those of other students’ [19][20]. We also found some cases where the students, in their junior years, talked about their experiences of the dynamic jigsaw as a source of acquiring various research skills (e.g., taking notes, writing reports, and questioning) [15]. These are encouraging signs for further exploration of the conditions that make collaborative learning situations more productive and beneficial to those involved.

20

N. Miyake / Designed Collaboration as a Scaffold for Schematic Knowledge Integration

Acknowledgments This series of research has been supported by SORST/JSP 2005-2007 and JSPS Grant-in-Aid 15200020 (2004-2006). References [1] Brown, A. (1992) Design experiments: Theoretical and methodological challenges in creating complex interventions. Journal of the Learning Sciences, 2(x), 141-178. [2] Collins, A. (1992) Toward a design science of education. E. Scanlon and T. O’Shea (eds.), New directions in educational technology, Springer-Verlag, Berlin. [3] Bransford, J., D., Brown, A. L., and Cocking, R. R. (1999). How people learn: Brain, mind, experience, and school (Expanded edition). National Academy Press, Washington, D.C.. [4] Bransford, J. D., & Donovan, S. (2005). How students learn. National Academy Press, Washington, D.C.. [5] Chi, M.T.H., Siler, S., Jeong, H., Yamauchi, T., & Hausmann, R.G. (2001). Learning from tutoring. Cognitive Science, 25:471-533. [6] Linn, M. C., & Hsi, S. (2000). Computers, teachers, peers: Science learning partners. Erlbaum. [7] Linn, M. C., Davis, B. A., & Bell, P. (Eds.) (2004). Internet environments for science education. Erlbaum, [8] Miyake, N. 1986. Constructive interaction and the iterative processes of understanding. Cognitive Science, 10(2), 151-177. [9] Scardamalia, M., & Bereiter, C., (1991). Higher-levels of agency for children in knowledge building: A challenge for the design of new knowledge media. The Journal of the Learning Sciences, 1(1), 37-68. [10] Shirouzu, H., Miyake, N., & Masukawa, H. (2002). Cognitively active externalization for situated reflection. Cognitive Science, 26(4), 469-501. [11] Roschelle, J. 1992. Learning by collaborating: convergent conceptual change. The Journal of the Learning Sciences, 2, 235-276. [12] Hatano, G., and Inagaki, K. (1991) Sharing cognition through collective comprehension activity. L. Resnick, J. Levin, and S. D. Teasley (eds.), Perspectives on socially shared cognition, American Psychological Association, Washington, D.C., 331-348. [13] Hatano, G., & Inagaki, K. (1986). Two courses of expertise. In H. Stevenson, H. Azuma & K. Hakuta (Eds.), Child development and education in Japan. Freeman & Co., 263-272. [14] Miyake, N., (2001), “Collaboration, technology and the science of learning: Teaching cognitive science to undergraduates,” The Annual Report of Educational Psychology in Japan, 40, 218-228. [15] Miyake, N. (2005a) Multifaceted Outcome of Collaborative Learning: Call for Divergent Evaluation. Paper presented at the meeting of the 13th International Conference on Computers in Education (ICCE2005), Singapore. [16] Miyake, N. (2005b) How can Asian educational psychologists contribute to the advancement of learning sciences?. Invited talk at the meeting of the Korean Society of Educational Psychology 2005 International Conference, Seoul, Korea. [17] Miyake, N., & Shirouzu, H. (2004) Learning from lectures for comprehension. Paper presented at the meeting of International Conference of the Learning Sciences 2004, Los Angeles, CA. [18] Miyake, N. and Shirouzu, H. (2006) A collaborative approach to teach cognitive science to undergraduates: The learning sciences as a means to study and enhance college student learning. Psychologia, 49, 2, 101-113. [19] Miyake, N., Shirouzu, H., & Chukyo Learning Science Group. (2005) Interactive learning cycles to foster knowledge integration. Paper presented at the meeting of the Germany-Japan Joint Workshop 2005, Tokyo. [20] Miyake, N., Shirouzu, H., and Chukyo Learning Science Group. (2005) The dynamic jigsaw: repeated explanation support for collaborative learning of cognitive science. Paper presented at the meeting of the 27th annual meeting of the Cognitive Science Society, Stresa, Italy. [21] Shirouzu, H, & Miyake, N. (2005) “CSCL for lecture comprehension and question asking: Commentable Movie Sheet on BBS”, Computer Supported Collaborative Learning 2005, Taipei, Taiwan. [22] Aronson, E., and Patnoe, S, (1997). The jigsaw classroom: Building cooperation in the classroom. Longman, New York.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

21

Technology Affordances for Intersubjective Meaning-Making Daniel SUTHERS Department of Information and Computer Sciences, University of Hawai`i, USA [email protected]

Abstract: The broad field of “computers in education” includes a diversity of approaches to using computers for learning. Each approach is based on an epistemology: a theory of how knowledge is gained. In this presentation, I will characterize the uses of technology and their corresponding epistemologies. I will single out intersubjective epistemologies as timely for research and practice, and call for development of technologies that offer social affordances and resources for meaning-making. The study of intersubjective meaning-making requires interactional analyses, but in new forms that transcend some of the assumptions and limitations of microanalysis and that can be coupled with other methodologies. The presentation illustrates these ideas with my research program on representational affordances for collaborative learning. Keywords: epistemologies of learning, representational guidance, technology affordances, interactional construction of meaning, research agenda

1

Introduction

The broad field of "computers in education" includes a diversity of approaches to using computers for learning. For example, we can find technology used as a publication medium, to present information or problems; as task-oriented tools for aiding performance, keeping track of information and organizing the learner's activities; as conceptual tools for relating features of problem instances to useful abstractions or expressing and testing the learner’s own ideas; as a communication medium through and with which learners engage with each other in peer tutoring, argumentation, or collaboration in making sense of a situation; and as a proxy for the teacher, selecting the next problem or activity, selecting learning partners, giving hints or correcting errors during performance, and confirming or correcting learners’ solutions. Each of these approaches is based on assumptions concerning learning and how technology can support it. These assumptions should be identified and used to guide design in a dialogue between theory and practice [26]. New forms of technology-mediated learning are possible if we re-examine our beliefs about learning and the roles of media in learning. My keynote presentation will provide an overview of my own quest. This extended abstract outlines the ideas to be covered and provides a bibliography for those who wish to pursue some of these ideas further. I first summarize relevant theories of how knowledge is gained, called epistemologies. I then single out intersubjective epistemologies as most timely for research and practice, and suggest lines of investigation into social affordances through which technology media can serve intersubjective meaning-making at various scales. The reader is referred to [30] for a more developed account of the material of this presentation, focused on the field of computer-supported collaborative learning (CSCL).

22

2

D. Suthers / Technology Affordances for Intersubjective Meaning-Making

Epistemologies of Learning

When the actual practices of our field are examined, we find that a great deal of work is based on a knowledge-communication epistemology. Knowledge communication is “the ability to cause and/or support the acquisition of one’s knowledge by someone else, via a restricted set of communication operations” [39]. Research conducted under this epistemology examines how to more effectively generate or facilitate communications that “cause and/or support” the desired acquisition of knowledge. The best work in this paradigm (e.g., [2]) eschews a simplistic view of learning as the transfer of information from outside to inside the learner's head, and treats knowledge communication in the context of constructivist and interactional stances, considered below. A constructivist epistemology [21, 37] emphasizes the agency of the individual learner in the learning process. Learning can only happen through the learner’s efforts at making sense of the world, although a mentor might arrange for the learner to have challenging experiences in order to accelerate the change process. Most researchers do not take constructivism to its solipsistic extreme, but instead view social interaction as helpful and even essential. Interactional epistemologies are diverse, and include accounts that emphasize both individual and social agency. With individual agency, the individual is the unit and agent of learning yet this learning can be enhanced through social interaction. Examples include cognitive dissonance theory [8] and socio-cognitive conflict theory [6]. Contribution theory [3] is interactional in its account of the construction of “common ground,” but is yet based on an individual epistemology as it does not explain how knowledge that did not predate the communication is jointly constructed within the communication process. At the boundary of individual and social agency, we find Vygotsky’s [38] oft-cited observation that developmental learning through social interaction can be understood as the internalization of interpersonal processes as intrapersonal processes. Intersubjective epistemologies are interactional epistemologies with social agency: they locate meaning-making and even learning at the group level. In a distributed or group cognition account, the group and its cultural/technological artifacts collectively constitute the proper unit of analysis [11, 28]. Knowledge and meaning can be understood as jointly created through interaction: learning consists of this interaction [16]. An intersubjective epistemology is distinguished from grounding in that interpretations emerge within the interaction, and so are shared from the outset. Learning is also conceived of as a community level phenomenon. A participatory epistemology sees learning as a process of increasing participation in the practices of a community [19], constructing personal and collective identity [40]. Another community level epistemology is knowledge building [25], the enterprise in which a community intentionally expands its cultural capital by reflecting on limits of understanding and choosing actions that address these limitations. 3

Intersubjective Meaning-Making

In my own analysis of CSCL [30], I single out intersubjective epistemologies as those that we most need to understand, at both the interpersonal and community levels. Given the pervasive social nature of learning, I maintain that this emphasis is of importance for other research communities such as the ICCE community. Intersubjective epistemologies lead to challenging unanswered questions. How is it possible for learning, usually conceived of as a cognitive function, to be distributed across people and artifacts [24]? What is the relationship of the change process we call “individual learning” to that individual’s participation in socially accomplished learning? The study of intersubjective learning is

D. Suthers / Technology Affordances for Intersubjective Meaning-Making

23

needed because we already have a substantial body of work on individual learning and on how the cognitive processes of participants are influenced by social interaction, while intersubjective learning is currently not prominent as a topic of study in our field (notable exceptions include [1, 16, 23, 27]). An intersubjective perspective will help designers understand how technologies can function as mediating resources in learning. In [30] I argue that “learning” is a judgment we make about the consequences of an activity, and to understand this accomplishment we must necessarily study the practices (the activity itself) of intersubjective meaning-making: how people in groups make sense of situations and of each other. 4

Implications for a Research Agenda on Social Affordances of Technology

In [30] I identify two distinct ways in which technology is applied to support collaborative learning—as a communication medium and as constraint (see also [10].) Both paradigms are limiting from an intersubjective meaning-making perspective, but both can contribute to a synthesis. Richer communication media are needed, particularly with respect to supporting the indexical nature of human communication [20]. Guidance for a learning agenda is needed for both discipline-specific practices and learning trajectories and for processes of intersubjective meaning-making, but without limiting creativity by excessively rigid scripting of action. In order to achieve advancements in these forms of support, we need to better understand the ways in which practices of meaning-making in the context of joint activity are mediated through designed artifacts [15] and apply this understanding to design fundamentally social technologies that are informed by the affordances and limitations of those technologies for mediating intersubjective meaning-making. The remainder of this paper identifies some unique social affordances of computational technology for intersubjective meaning-making, suggesting lines of investigation in research and design. Negotiation Potentials. Any medium offers certain potentials for action. Participants may feel an obligation to obtain agreement on modifications to shared workspaces. The potentials for action offered by the medium can therefore guide interactions towards ideas associated with the afforded actions [34]. If we would like users of our technology medium to focus on particular aspects of a problem, how can the medium be designed to prompt for actions that require negotiation of these aspects? Referential Resource. Jointly constructed representations become imbued with meanings for the participants by virtue of having been produced through a process of negotiation. These representational constituents then become a rich referential resource for conversation [33], facilitating elaboration on previous conceptions. Rather than being vehicles for communicating expert knowledge, representations become objects about which learners engage in sense-making conversations [23] and can be designed to lead to productive conversation. How can we make salient that which learners would productively interpret, elaborate on and relate to new information or ideas? Integration. The computational medium can leave a persistent record of activity [5]. How can traces of interaction and collaboration be designed to foster appropriate awareness of prior conceptions and the means to reference these in subsequent interactions so that they may be integrated with new information and ideas? (Im)mutable Mobiles. The mobility of digital inscriptions provides opportunities for recruitment of partners in the sense-making process [18] and supports continued engagement in that process. How can we exploit this property of technology for its potential to make new social alignments and their interactions possible?

24

D. Suthers / Technology Affordances for Intersubjective Meaning-Making

Reflector of Subjectivity. Computational media can be designed to foster group awareness [17], visualize conflict or agreement between members [12], or project representations of self into a social representation [14]. In what ways can we design technology to mediate intersubjectivity by reflecting activity, subjectivity, and identity? Trajectories of Participation. Technologies offer social affordances for patterns of participation over larger spans of time and collections of actors [22]. How can we encourage productive entanglement of multiple individual trajectories of participation by selectively making their contributions salient and hence available for subsequent interpretation by others? My colleagues and I have been engaged in work on social affordances of technology since we first realized that the visualizations and coach of Belvedere had significance primarily (if at all) in how they affected peer interaction [36]. This began a line of work on representational guidance [31] that investigated negotiation potentials [34], referential resources, and integration [33]. More recently, we have brought this work to an asynchronous paradigm [35], and have begun a new line of work examining the fundamental practices by which people appropriate the affordances of certain media for written communication [7]. 5

Analysis of Intersubjective Meaning-Making

Although some of our prior work has been in a quantitative experimental paradigm, we have found that the study of intersubjective meaning-making requires coordinated use of qualitative interactional analyses [4]. Quantitative methods aggregate over many sessions, obscuring the actual procedures by which participants accomplish learning through the affordances of online media [16]. Methods for studying the interactional construction of meaning are available [13, 9], but have largely been developed for brief episodes of face-to-face data, and do not scale well to online learning where media resources, time scale, and synchronicity all differ. This analytic tradeoff between scalability and fidelity must be resolved in order to inform the design of improved online learning environments and participation structures that engage participants more deeply in intersubjective meaning-making during collaborative inquiry. My research group has been working on this problem for several years now [29]. As our current progress is reported elsewhere in this proceeding [32], it will not be detailed here. The short-term objective of the work reported in [32] is to scale up sequential and interactional analysis to distributed and asynchronous interactions while remaining grounded in participants' use of media. The long-term objective of the entire enterprise discussed in this extended abstract is to obtain a deep understanding of how learning is accomplished interactionally in technology-mediated setting, and then to offer learners environments that provide the resources and guidance they need for engaged learning. Acknowledgments This work was supported by the National Science Foundation under award 0093505. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.

References [1]

[2]

M. Baker, Computer-mediated argumentative interactions for the co-elaboration of scientific learning tasks., in Andriessen, Baker and Suthers, eds., Arguing to Learn: Confronting Cognitions in Computer-Supported Collaborative Learning Environments., Kluwer, Dordrecht, 2003, pp. 47-78. R. Bromme, R. Jucks and A. Runde, Barriers and biases in computer-mediated expert-layperson communication: An overview and insights into the field of medical advice, in R. Bromme, F. W. Hesse

D. Suthers / Technology Affordances for Intersubjective Meaning-Making

[3]

[4] [5]

[6] [7]

[8] [9] [10]

[11] [12]

[13] [14]

[15] [16]

[17]

[18] [19] [20] [21] [22] [23]

[24] [25]

[26] [27] [28]

25

and H. Spada, eds., Barriers and Biases in Comptuer-Mediated Knowledge Communication -- And How They May Be Overcome, Springer, New York, 2005, pp. 89-118. H. H. Clark and S. E. Brennan, Grounding in communication, in L. B. Resnick, J. M. Levine and S. D. Teasley, eds., Perspectives on Socially Shared Cognition, American Psychological Association, 1991, pp. 127-149. J. W. Cresswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, Sage Publications, 2003. P. Dillenbourg, Designing biases that augment socio-cognitive interactions, in R. Bromme, F. W. Hesse and H. Spada, eds., Barriers and Biases in Computer-Mediated Knowledge Communication—and How They May Be Overcome, Springer, New York, NY, 2005, pp. 243-264. W. Doise and G. Mugny, The Social Development of the Intellect, International Series in Experimental Scoial Pscychology, Pergamon Press, 1984. N. Dwyer and D. D. Suthers, A Study of the foundations of artifact-mediated collaboration, in T. Koschmann, D. D. Suthers and T.-W. Chan, eds., Computer Supported Collaborative Learning 2005: The Next 10 Years!, Lawrence Erlbaum Associates., Mahwah, NJ, 2005, pp. 135-144. L. Festinger, A Theory of Cognitive Dissonance, Stanford University Press, Stanford, 1957. C. Goodwin and J. Heritage, Conversation Analysis, Annual Review of Anthropology, 19 (1990), pp. 283-307. T. Hansen, L. Dirckinck-Holmfeld, R. Lewis and J. Rugelj, Using telematics for collaborative knowledge construction, in P. Dillenbourg, ed., Collaborative Learning: Cognitive and Computational Approaches, Elsevier, Amsterdam, 1999, pp. 169-196. E. Hutchins, Cognition in the Wild, The MIT Press, Cambridge, Massachusets, 1995. P. Jermann and P. Dillenbourg, Elaborating new arguments through a CSCL script, in Andriessen, Baker and Suthers, eds., Arguing to Learn: Confronting Cognitions in Computer-Supported Collaborative Learning Environments, Kluwer, Dordrecht, 2003, pp. 205-226. B. Jordan and A. Henderson, Interaction Analysis: Foundations and practice, The Journal of the Learning Sciences, 4 (1995), pp. 39-103. J. Kaput and S. Hegedus, Exploring classroom connectivity by aggregating student constructions to create new learning opportunities, in A. D. Cockburn and E. Nardi, eds., 26th Annual Conference of the International Group for the Psychology of Mathematics Education, UK, 2002. T. Koschmann, Dewey's contribution to the foundations of CSCL research, Proc. Computer Supported Collaborative Learning 2002, Boulder, 2002, pp. 17-22. T. Koschmann, A. Zemel, M. Conlee-Stevens, N. Young, J. Robbs and A. Barnhart, How do people learn: Member's methods and communicative mediation, in R. Bromme, F. W. Hesse and H. Spada, eds., Barriers and Biases in Computer-Mediated Knowledge Communication (and how they may be overcome), Kluwer Academic Press, Amsterdam, 2005, pp. 265-294. K. Kreijns and P. A. Kirschner, Designing sociable CSCL environments, in J. W. Strijbos, P. A. Kirschner and R. L. Martens, eds., What We Know About CSCL and Implementing it in Higher Education, Kluwer, Dordrecht, 2004, pp. 221-243. B. Latour, Drawing things together, in M. Lynch and S. Woolgar, eds., Representation in Scientific Practice, The MIT Press, 1990, pp. 19-67. J. Lave and E. Wenger, Situated Learning: Legitimate Peripheral Participation, Cambridge University Press, Cambridge, 1991. G. Nunberg, Indexicality and deixis, Linguistics and Philosophy, 16 (1993). J. Piaget, The Grasp of Consciousness: Action and Concept in the Young Child, Harvard University Press, Cambridge, MA, 1976. P. Resnick, Beyond Bowling Together: SocioTechnical Capital, in J. M. Carroll, ed., Human-Computer Interaction in the New Millennium, ACM Press, Upper Saddle River, NJ, 2002, pp. 647-672. J. Roschelle, Designing for cognitive communication: Epistemic fidelity or mediating collaborating inquiry, in D. L. Day and D. K. Kovacs, eds., Computers, Communication & Mental Models, Taylor & Francis, London, 1996, pp. 13-25. G. Salomon, ed., Distributed Cognitions: Psychological and Educational Considerations, Cambridge University Press., Cambridge, 1993. M. Scardamalia and C. Bereiter, Knowledge Building Environments: Extending the Limits of the Possible in Education and Knowledge Work, Encyclopedia of Distributed Learning, Sage Publications Thousand Oaks, CA, 2003. D. A. Schön, The Reflective Practitioner, Basic Books, New York, 1983. G. Stahl, Group cognition in computer-assisted collaborative learning, Journal of Computer Assisted Learning, 21 (2005), pp. 79-90. G. Stahl, Group Cognition: Computer Support for Collaborative Knowledge Building, MIT Press, Cambridge, MA, 2006.

26

D. Suthers / Technology Affordances for Intersubjective Meaning-Making

[29] D. D. Suthers, A qualitative analysis of collaborative knowledge construction through shared representations Research and Practice in Technology Enhanced Learning 1(2006), pp. 1-28. [30] D. D. Suthers, Technology affordances for intersubjective meaning-making: A research agenda for CSCL, International Journal of Computers Supported Collaborative Learning, 1 (2006), pp. (in press). [31] D. D. Suthers, Towards a systematic study of representational guidance for collaborative learning discourse, Journal of Universal Computer Science, 7 (2001). [32] D. D. Suthers, N. Dwyer, R. Medina and R. Vatrapu, Analysis of Meaning Making in Online Learning, International Conference for Computers in Education, APSCE, Bejing, 2006. [33] D. D. Suthers, L. Girardeau and C. Hundhausen, Deictic roles of external representations in face-to-face and online collaboration, in B. Wasson, S. Ludvigsen and U. Hoppe, eds., International Conference on Computer Support for Collaborative Learning 2003, Kluwer Academic Publishers, Dordrecht, 2003, pp. 173-182. [34] D. D. Suthers and C. Hundhausen, An experimental study of the effects of representational guidance on collaborative learning, Journal of the Learning Sciences, 12 (2003), pp. 183-219. [35] D. D. Suthers, R. Vatrapu, R. Medina, S. Joseph and N. Dwyer, Beyond threaded discussion: Representational guidance in asynchronous collaborative learning environments, Computers & Education (to appear). [36] D. D. Suthers and A. Weiner, Groupware for developing critical discussion skills, First International Conference on Computer Support for Cooperative Learning, Bloomington, IN, 1995. [37] E. Von Glasersfeld, A Constructivist Approach to Teaching. , in L. S. J. Gale, ed., Constructivism in Education, Lawrence Erlbaum Associates, Inc., New Jersey, 1995, pp. 3-16. [38] L. S. Vygotsky, Mind in society, Harvard University Press, Cambridge, MA, 1978. [39] E. Wenger, Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge, Morgan Kaufmann, Los Altos, 1987. [40] E. Wenger, Communities of Practice: Learning, Meaning and Identity, Cambridge University Press, Cambridge, 1998.

Modeling and Representation

This page intentionally left blank

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

29

Design of an Environment for Developing Presentation Skills a

Kazuhisa Setaa and Mitsuru Ikedab Graduate School of Sciences, Osaka Prefecture University, Japan b School of Knowledge Science, JAIST, Japan [email protected], [email protected]

Abstract: Presentation plays an important role in transmitting one’s opinion and encouraging collaborative knowledge creation and decision-making processes. A key to producing persuasive presentation materials is to perform meta-cognitive activities well, but that is difficult for novice learners of presentation tasks. Furthermore, from the system-development viewpoint, it is difficult to develop a learning system with which learners can develop their presentation skills effectively because the cognitive activities in presentation task are typically not clarified. In this paper, we first present a cognitive model of the user who performs presentation task. We then overview two kinds of designed environments based on that model: one is for producing presentation materials that encourage learners to perform meta-cognitive activities; the other is a collaborative learning environment in presentation rehearsal, which encourages the transfer of context-dependent meta-cognitive knowledge among learning partners. Keywords: meta-cognition, scaffolding, presentation task, presentation skill, cognitive model

1. Introduction Presentation plays important roles in transmitting one’s opinions and encouraging collaborative knowledge creation and decision-making processes. In business scenarios, results of presentations often influence important administrative decision-making. It is not too much to say that the presentation skills that enable a person to organize their opinions and transmit them adequately are necessary skills for various applications in business, research, learning, and so on. In western countries, lessons related to rhetoric, speech, and presentation that enable a person to transmit one’s opinion adequately are provided [3][8]. Recently, such lessons have been reintroduced gradually into the university curriculum; numerous books on the subject are published even in Japan. These phenomena reflect the need for fostering well-developed presentation skills. Presentation processes, in general, consist of many activities such as understanding of audiences’ requirements for the presentation, setting presentation objectives and goals, and presentation design. Know-how of these activities can be partially externalized, i.e., general requirements satisfied by the result of each activity and what kind of (sub-)actions should be undertaken by the presenter are described explicitly in a document. The user can therefore acquire such fundamental knowledge. Nevertheless, the know-how strongly depends on one’s presentation scene: the way to build a consistent presentation logic, translate it to presentation sequences in an easily acceptable format for the audience, and build accurate audience models cannot be described explicitly in a document. Therefore, the user must develop such skills through experience.

30

K. Seta and M. Ikeda / Design of an Environment for Developing Presentation Skills

Document media that are written in natural language are low-cost and high-performance media for knowledge transmission and spreading a viewpoint. Fundamental rules for presentations are actually written in the document media. However, the describable principle of know-how in a document is essentially generic. Consequently, (1) it is often difficult to specify and adapt to the individual specific scene. In contrast, the described case, which is dependent on know-how in a document, is fundamentally specific. For those reasons, (2) it is often difficult to generalize and transfer it to other individual-specific scenes. Furthermore, (3) it is difficult to determine the descriptions even when appropriate descriptions should be referred. Moreover, because one’s constructed presentation know-how is rooted in experiences and its externalization is strongly influenced by the author’s subjective intentions and viewpoint, (4) the described explicit know-how is often subjective. It is therefore necessary (a) to analyze and clarify the principle knowledge for making high-quality presentations and (b) to develop a learning environment based on them whereby a learner can develop presentation skills through the user’s presentation experiences. Our research goal is to clarify and systematize presentation skills as a presentation skill ontology and to build a support environment with which a novice learner of presentations can produce persuasive presentation materials and develop the required skills. In this paper, we describe a design of a CSCL environment for producing presentation materials as a first step towards achieving our goal. 2. Cognitive Model in Presentation Task Through our surveys of presentation know-how, we have inferred that performing meta-cognitive activities [1][2] such as self-monitoring, self-evaluation, and reflection, plays an important role to produce consistent and persuasive presentation materials and to develop presentation skills continuously. Figure 1 shows a cognitive model that incorporates detailed working processes of a learner that we adopt as a reference model for our system design. We capture presentation tasks as a form of problem-solving oriented learning a learning style in which a learner must not only accumulate sufficient understanding of a subject domain but also acquire the capacity for constructing effective problem-solving and learning processes according to a sophisticated strategy [5][6][7]. In the presentation task, processes of creating presentation materials correspond to problem-solving processes. Figures 1(i) and 1(iii) correspond to the planning process of the presentation plan and learning plan in Fig. 1, respectively, and 1(viii) and 1(x) respectively correspond to presentation rehearsal processes and learning processes in Fig. 1. Figures 1(v), 1(vi) and 1(xi) correspond to the monitoring process. Figure 1(xii) represents performing processes before the audience. We have presented a subject of presentation, say, ‘make a presentation for explaining the role of UML in software development processes’. Two virtual persons in the user, a presentation planner and learning process planner, respectively function in the roles of planning, monitoring and controlling problem-solving, and learning processes. Through the presentation task, a learner first defines a presentation goal with audience models and refines it to sub-goals that contribute to achieving goal G (Fig. 1(i), 1(vii)). They are refined to feasible presentation plans (Fig. 1(ii)); thereafter, the learner performs them to achieve presentation goals (Fig. 1(viii)).

K. Seta and M. Ikeda / Design of an Environment for Developing Presentation Skills

31

Fig. 1. Overview of Cognitive Model in Presentation Task The user generates an adequate learning goal (LG) to gain knowledge (Fig. 1(iii)) if the user recognizes a lack of knowledge in sub-goaling and rehearsing presentation plans. The user then might refine it to learning process plans (Fig. 1(iv)). In learning processes (Fig. 1(x)), that learner constructs knowledge (Fig. 1(iv)) that will be required for planning and performs the presentation process. Based on constructed knowledge, the user specifies presentation plans with audience models, creates presentation materials, and rehearses the presentation processes (Fig. 1(viii)) that alter the audience world (Pseudo Audience, Fig. 1(vii)). The user assesses gaps among goal states (GS), current goal states (CGS) of presentation process plans, and the current state (c-state) of the pseudo-audience (Fig. 1(v)) and those among learning goal states (LGS), current learning goal states (CLGS) of learning process plans, and the understanding state (Fig. 1(vi)). The user iterates these processes until the c-state of the real world satisfies the GS of problem solving. It is noteworthy that learners in presentation tasks must produce and execute not only plans for making presentations; they must also learn plans in the process. Furthermore, it is important for them to monitor audience model changes by rehearsing presentation processes and to monitor their own understanding states by performing learning processes and analyzing whether states of the pseudo audience satisfy the defined goal states (Figs. 1(v) and 1(vi)). The gap between current states and goal states dissolve the definition of new goals. Consequently, there exist two types of meta-cognition that a learner must perform in the presentation task. One is self-monitoring and (re-)construction of one’s own understanding states. Performing these meta-cognitive activities encourages building of a deep understanding of the target and clear logic of the presentation. It therefore plays an important role in building the foundation of a consistent and persuasive presentation. The other is self-monitoring and (re-)constructing the audience model by referring to one’s own understanding states (cognition of other people). Performing these

32

K. Seta and M. Ikeda / Design of an Environment for Developing Presentation Skills

meta-cognitive activities adequately encourages the setting of reasonable goals, scope, and a presentation grain size. It therefore plays an important role in the design of sophisticated presentations that are acceptable to the audiences. Presenters must perform these meta-cognitive activities accurately to create consistent and persuasive presentation materials; therefore, presentation tasks compel a learner to perform complicated tasks with heavy cognitive loads. A learner must manage and allocate attention resources adequately because of limited human attentional capacity. This explains why a novice learner tends to fall into confusion and learn ineffectively. Novice learners of a presentation task tend to allocate much attention to cognitive activities, especially to material-making processes or representations of materials in general. 3. System Design to Scaffold the Development of Presentation Skills Many authors describe the necessity and importance of performing these meta-cognitive activities in their books, but they do not include the term or concept of meta-cognition. Nevertheless, it is difficult for a novice presenter to learn when, how, and what kind of meta-cognitive activities should be performed in actual processes of presentation-material preparation because meta-cognitive activity is essentially tacit and latent; performing these meta-cognitive activities requires expertise of causal relations among the order of explanation, the presentation grain size, audience characteristics, and how the presentation affects the audience members’ thinking. It is therefore necessary to develop a learning environment in which a learner can improve working knowledge of performing meta-cognitive activities through practice while grasping fundamental principles of presentation through traditional lessons or books. 3.1 Design Principle Our system is designed to realize a learning environment (a) that encourages learners’ spontaneous meta-cognition required to make high-quality presentation materials and, in which (b) a learner can use presentation-material preparation experiences as valuable learning resources, and in which (c) a learner can include expertise of other experienced learners effectively. Based on the cognitive model presented in the previous section, we designed three kinds of learning environment: a learning environment for presentation-material preparation phase, presentation rehearsal phase and post-presentation (reflection) phase. Thus, the user can learn through all presentation processes. We describe the learning environment for the presentation-material preparation phase and the presentation rehearsal phase as follows. 3.2 Learning Environment for Material Preparation Phase Figure 2 shows a learning environment for the presentation-material preparation phase. The environment consists of five interface windows. Figure 2(iii) is a window of a presentation preparation computer program (Power Point, PPT; Microsoft Corp.), which is familiar to many people throughout the world; other windows are designed to function together with the PPT. For example, when the user selects a PPT slide, then domain relations, logical relations, presentation goals, and knowledge resources that are related to the slide are shown and highlighted in each window. Figure 2(i) is named the domain structure view, in which a learner can describe the relations of domain concepts that the user learned in relation to the presentation target. For

K. Seta and M. Ikeda / Design of an Environment for Developing Presentation Skills

33

example, the user can describe that “UML plays an important role in enhancing communication among humans who attend the software lifecycle” in the view of using graphical representations (nodes and arcs) and natural language. Furthermore, the user can color each concept (node) in white when the user sufficiently understands the concept. Figure 2(ii) is named a logical relation view in which the user can derive and arrange the logical relations of arguments hierarchically by referring to concept relations described in the domain structure view. For example, the user can describe logical relations that the reason of “UML plays an important role in the software lifecycle” are that “UML provides a common language that is needed throughout the entire software lifecycle” and “it encourages smooth communication among humans who attend the lifecycle” in the view. Figure 2(iv) is named the intention view, in which the user can design a rational presentation structure that is acceptable to audiences to achieve presentation goals by referring to the described domain relations, logical relations, and presentation goal vocabulary, which are shown in the left part of the view. For example, the user can describe the presentation goal of “make audiences understand the roles of UML in software lifecycle” and then specify it to detailed sub-goals, as “make audiences understand the overview of software lifecycle by introducing the concept of software lifecycle” then “make audience understand the roles of UML in each process of software lifecycle by showing an example scenario.” In this case, domain-independent and presentation-task-specific exemplary scenarios that introduce, reject, or overview can be used for describing the presentation goals. They are specified in the presentation goal vocabulary described above. We specify them by surveying popular books and on-line resources related to presentation know-how [3][8][9][10] and by referring to the learning support systems’ goal ontology proposed by Hayashi et al. [4]. Furthermore, the user can specify intentional effects to audiences in greater detail using graphical representations in lower parts of the view.

Fig. 2: Designed learning environment for presentation-material design

34

K. Seta and M. Ikeda / Design of an Environment for Developing Presentation Skills

Figure 2(v) portrays knowledge resources. Because the user can bookmark them by connecting with related domain concepts in (i), arguments in (ii), and the slides in (iii), the user can refer to them easily by clicking the nodes or slides in respective windows. The salient advantage of this learning environment from the viewpoint of the improvement of presentation quality and developing her presentation skill is expected to be that the environment encourages learners’ spontaneous meta-cognition during the presentation-material preparation processes. Visually externalized information in (i) and (ii) encourages self-monitoring and confirms processes of the user’s own understanding and self-control processes. Furthermore, visually externalized information in (iv) encourages self-monitoring and confirmation processes of rationality between a goal and its sub-goals and motivates the user to be conscious of constructing an accurate audience model. The described information represents users’ intentions for the presentation. Consequently, it is used as a valuable learning resource for subsequent collaborative learning in the presentation rehearsal phase. 3.3 CSCL Environment for Presentation Rehearsal It is difficult for novice learners to be aware of mistakes embedded in their presentation materials because of the lack of other persons’ viewpoints about it. Furthermore, it is hard for another person to give appropriate instructions to the user because of the difficulties in inferring the creator’s design intentions. It is also difficult to teach knowledge of making sophisticated presentation materials in a general context that includes a variety of meta-cognitive knowledge: it is quite tacit, latent, and highly context-dependent. For those reasons, we designed a collaborative learning environment in which the user and the instructor can share not only presentation material but also learners’ intentions (rich contexts) behind the presentation materials, which encourages well-focused communications among learners. Consequently, it encourages smooth knowledge transfer. Figure 3 shows a collaborative learning environment for presentation rehearsal and reflection phase. Both the presenter and learning partners (playing the audience role) can share the same information shown in the figure. In this environment, the presenter can examine each component of the presentation materials collaboratively or conduct a rehearsal using text-based or speech-based chat. The environment includes three windows. Figure 3(i) is designated as the presentation view based on a PPT slide show. Both the presenter and learning partners (LPs) can advance the slides. Figure 3(ii) is an intention view, in which LPs can refer and understand the presenter’s intentions of the slides set in the presentation-material preparation phase: roles of the slides from the viewpoint of achieving the presentation goals described above, subjects of the slide, what kinds of reactions the presenter expects from audiences, and so on. By clicking a presentation goal node, participants can advance and review a series of slides that are intended to achieve the clicked presentation goal. Furthermore, LPs can evaluate each presentation material by checking the items shown in the lower part of the view. These items are constructed automatically using the system based on the described presentation goal hierarchy. Figure 3(iii) is a text-based chat window with which participants can communicate with each other. Generally speaking, it is difficult to evaluate the presentation materials because of the difficulties in understanding the presenter’s intentions. The major advantage of this collaborative learning environment is that the participants can refer to the presenter’s presentation goals and intentions in addition to presentation materials. This encourages smooth discussions that specifically address whether the presenter’s intentions are valid,

K. Seta and M. Ikeda / Design of an Environment for Developing Presentation Skills

35

Fig. 3: Designed collaborative learning environment for presentation rehearsal whether presentation materials adequately reflect the described intentions and so forth, because LPs can have a viewpoint for evaluation and discussion. For example, a presenter’s intention of the slide shown in Fig. 3(i) is described in the upper right part of Fig. 3(ii) in natural language as “understanding the overview of roles and merits of using UML in the software lifecycle.” Furthermore, the user’s intentions of achieving this presentation goal contribute to realization of the upper presentation goal of “explaining the roles of UML in software lifecycle” are represented graphically in the presentation goal hierarchy. Based on the goal hierarchy, the system can construct items for evaluation: “The presenter intended to explain an overview of roles and merits of using UML in software lifecycle. Can you understand?”, “Is this slide valid from the viewpoint of introducing the UML overview?” and so on. The LPs can evaluate the slide according to the evaluation items. The evaluation results are recorded and sent to the presenter. Furthermore, participants can discuss and send their suggestions: “I think it is better to explain the difficulties of each process in greater detail to achieve your goal. Because the audience can understand the roles of UML in greater detail based on their deep understanding of the necessities of UML” and so on. This kind of well-focused discussion can be inspired by referring to presenter’s intentions and items for evaluation. By providing the CSCL environment proposed here, presenters can use experiences of presentation-material preparation as valuable learning resources for developing presentation skills.

36

K. Seta and M. Ikeda / Design of an Environment for Developing Presentation Skills

4. Conclusions and Future work This paper presented a description of the outline of our learning support environment for developing presentation skills. The major advantage of our system is that it is designed to encourage the presenter to perform meta-cognitive activities in presentation-material design and import expertise of other experienced learners in presentation rehearsal based on the cognitive model. It is important to produce the model assumed in the system design that is explicit not only for sophisticated learning environment but also for its continuous improvement. We were unable to describe the learning environment for review after performing a presentation, which would allow the user to analyze their presentations collaboratively. This issue will be examined in another paper. We hope to improve the presentation goal vocabulary and build presentation skill ontologies in our future work. Acknowledgments We are very thankful to Mr. Takashi Kajino and Masayoshi Toyokura for their kind help and efforts. This research was supported in part by a grant from the Matsushita Education Foundation. References

[1] Brown, A. L., Bransford, J. D., Ferrara, R. A., and Campione, J. C. (1983) Learning, Remembering, and Understanding, In: E. M. Markman and J. H. Flavell. (Eds.), Handbook of child psychology (4th ed.) Cognitive Development, Vol. 3, Wiley, 515–529. [2] Flavell, J. H. (1976) Metacognitive Aspects of Problem Solving, in L. Resnick (Ed.), The nature of intelligence. Lawrence Erlbaum Associates: Hillsdale, NJ, 231–235. [3] Harvard University Writing Center: http://writing2.richmond.edu/writing/wweb.html [4] Hayashi, Y., Ikeda, M., and Mizoguchi, R. (2004) A Design Environment to Articulate Design Intention of Learning Contents, International Journal of Continuing Engineering Education and Life Long Learning, 14, 3, 276-296. [5] Seta, K., Tachibana, K., Umano, M., and Ikeda M. (2003) Basic consideration on reflection in problem-solving oriented learning, Proc. of the International Conference on Computers in Education (ICCE-03), Hong Kong, China, 160-168. [6] Seta, K., Tachibana, K., Fujisawa I., and Umano M. (2004) An ontological approach to interactive navigation for problem-solving oriented learning processes, International Journal of Interactive Technology and Smart Education, 1, 3, 185-193. [7] Seta, K., Tachibana, K., Umano., M., and Ikeda, M. (2005) Human Factor Modeling for Development of Learning Systems Facilitating Meta-Cognition, in Chee-Kit Looi, David Jonassen, Mitsuru Ikeda (Eds): Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences, Frontiers in Artificial Intelligence and Applications, Vol. 133, IOS Press, 396-403, (also Proc. of the International Conference on Computers in Education (ICCE-05), Singapore). [8] Stanford University Writing Center: http://swc.stanford.edu/library.htm . [9] Takada, T. (2004) Logical Presentation, Eiji Press (in Japanese). [10] Teruya, H., and Okada. K. (2001) Logical Thinking, Toyo Keizai Inc (in Japanese).

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

37

Ontological Modeling Approach to Blending Theories for Instructional and Learning Design Yusuke HAYASHIa, Jacqueline BOURDEAUb, Riichiro MIZOGUCHIa a ISIR, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047 Japan b LICEF, Télé-université, 100 Sherbrooke W., Montréal, (QC) H2X 3P2 Canada [email protected] Abstract: This paper proposes a modeling framework for learning and instructional design from the viewpoint of ontological engineering. One of the characteristics of this framework is a theory/paradigm-independent ontology for modeling learning/instruction. This paper discusses how our modeling framework with the theory/paradigm-independent ontology contributes to modeling learning and instruction from a comprehensive viewpoint of various educational theories. Keywords: Instructional design, Learning design, Ontology, Theory-aware, Design support

Introduction Sharing and reuse of information about not only resources but also their structure such as design intention have been brought to public attention as recent development of IT standards in the areas of learning, education, and training. Representative specifications include IEEE LTSC Learning Object Metadata (LOM) standard [9] and IMS Learning Design (LD) specification [10]. These bring about global benefits of exchange format for sharing and reuse of information about learning contents. However, a problem still remains: how to build “good” design for education using the standards. Of course, considerable achievement has been made in instructional and learning sciences. This being said, even though some educational theories (learning, instructional and instructional design theories) prescribe optimal/desirable methods of learning and instruction, many of the theories are not sufficiently articulated for the use of designers. Such theories allow for diverse interpretation and therefore may be difficult to use in practice. One of the reasons why these problems come up is that the description of educational theories is made in natural language, using different terminology. As Reigeluth points out, although many theories prescribe the same method for the same situation, these are described in different terminology [18]. This leads to a diversity of theories that are all open to interpretation. Even for experts, it is sometimes difficult to appropriately use theories while having a clear understanding of the similarities and differences between them. Consequently, we must first establish a common basis for understanding the theories at a conceptual level, along with organized concepts and vocabulary. The goal of this study is to build an engineering infrastructure that enables designers to select instructional and learning theories and blend them into an instructional/learning design. This paper thus proposes a modeling framework for learning and instructional design from the view point of ontological engineering [14], based on the results of previous

38

Y. Hayashi et al. / Ontological Modeling Approach to Blending Theories

research in this respect [1][2][16] . In addition, the purpose of this study is not to expose a scientifically valid basis for organizing educational theories nor to reconstruct them on this basis, but rather to find an engineering approximation that allows the building of an engineering infrastructure that enables practitioners to utilize educational theories. This paper is organized as follows. The next section presents our perspective on modeling of learning and instruction and proposes our resulting modeling framework for them. Section 2 illustrates an example of a model of theoretical knowledge for education based on this modeling framework. Section 3 discusses an application of this framework to design support system for learning scenarios or activities. The final section concludes this paper and states the issues to be examined further.

1. Ontological modeling framework for instructional/learning design This study adapts the ontological modeling framework of functional design knowledge in the world of manufacturing industry by Kitamura et al. [12] to model educational theories. The main characteristic of this framework is independent conceptualization of what is achieved and how to achieve the change in the target things. The former is organized as ontologies of functionality, and the latter is as ways of function achievement. The function of artifacts (devices) is modeled as hierarchical structure of component functions linked with ways of function achievement. This model is called “function decomposition tree”. Kitamura et al. have confirmed the effectiveness of the framework in: (1) affording a better understanding of the functionality of devices, (2) facilitating the sharing of design rationales of devices, (3) supporting the improvement of functionalities, and so on. Although the domain is different from educational knowledge, we believe that it is applicable to the systematization of theoretical knowledge for instructional/learning design. For the educational domain, we have developed an ontology of learning, instruction and instructional design (L/I/ID) [1][2][16] . This study proposes an ontological modeling framework for education based both on the L/I/ID ontology and Kitamura’s framework [8]. It aims at facilitating (1) the sharing of a model of instruction/learning and (2) the application of educational theories appropriately to the modeling. IMS Learning Design (LD) specification [10] has recently focused on the sharing aspect, and we believe that our approach will contribute to make IMS LD specification work with educational theories. Roughly speaking, the L/I/ID ontology is composed of five major concepts: concepts related to Common, Learning, Instructional and Instructional design worlds and Educational event in the learning and the instructional worlds [8]. This ontology is developed in an effort to model learning and instruction in any paradigm - Behaviorism, Cognitivism and Constructivism -. Ertmer and Newby [6] assert that although each of the paradigms has many unique features, each describes the same phenomena (learning). In a similar line of thought, one of the notable features of this ontology is the conceptualization of relation between learning and instruction. The meaning of an instruction is defined by the change of learner state as achieved or by the intended result of learning. However, instruction is defined independently of the change of learner state. That is because an instructional action may have different effects on the change depending on the context. Independence is the key to allow for a variety of combinations of instructional actions and effects and to compare strategies provided by educational theories. The Learning/instruction process is modeled based on our ontological modeling framework for education. A unit of learning/instruction is described as I_L event, which is a sub-class of Educational event. An I_L event is defined as a combination of instructional action and change of learner state caused by a learning action. This definition allows to describe the relation between instruction and learning in a learning/instruction process. The

Y. Hayashi et al. / Ontological Modeling Approach to Blending Theories

39

(A) Preparing the learner for learning / Being ready to learn

Macro -I_L event

Way of achievement Preinstruction

(B)

is-achieved by

(C) Motivate / Motivated

Stimulate recall of prior learning / Recalling prior learning

I_L event decomposition

Micro -I_L events

Fig. 1 An example of I_L event decomposition whole process is modeled as a function decomposition tree. In our framework, this model is a hierarchical structure of I_L event to achieve a certain change of a learner state. Thus, it is called “I_L event decomposition tree”. In an I_L event decomposition tree, an upper (macro) I_L event is connected with the lower (micro) ones by way of achievement of change of a learner state (referred to just as “Way” hereafter). For example, consider a situation where an instructor wants a learner to be ready to learn and it is necessary that the learner is motivated into the learning and then that he recalls prior learning. Fig.1 illustrates this case as I_L event decomposition. An oval node represents an I_L event. Fig.1 (A) indicates the macro-I_L event, in which “Preparing the learner for learning” is the instructional action and “Being ready to learn” is the change of learner state. A conceivable process to achieve this is to motivate the learner (Fig. 1 (B)) and then to stimulate recall of prior learning (Fig. 1 (C)). The former instructional action brings about the learner state “Motivated” (Fig. 1 (B)), and the latter does “Recalling prior learning” (Fig. 1 (C)). A way is a description of relationship of such a decomposition of the required change into the detailed changes and actions to achieve them. A ‘Way’ has two sorts of interpretations. One, so-called bottom-up manner, is that the sum of the changes of learner state in micro-I_L event promotes the changes of learner state of the macro-I_L event. This manner, which concentrates on states, is descriptive. It describes which outcome is produced by a sequence of changes of learner state. The other, so-called top-down manner, is that an instructional action of a macro-I_L event is decomposed into detailed/concrete instructional actions of micro-I_L events. This manner, which concentrates on action, is prescriptive. It prescribes which sequence of instructional actions is required for performing the intended instructional action. Following the top-down interpretation, this study proposes a method to systematize theoretical knowledge. The theories prescribe strategies for planning instructional and learning process according to supposed situations. These strategies may superficially vary from theory to theory but, as discussed in the introduction, some of the differences just come from the difference in terminology that each theory uses. We assume that some essentials of theories characterizing themselves are disclosed if their strategies are resolved conceptually. In our framework we propose modeling learning/instructional strategies as Way using L/I/ID ontology. A set of Ways derived from a theory characterizes the theory from the prescriptive aspect. In addition, modeling strategies on a common basis, which is L/I/ID ontology, is expected to disclose not only characteristics of each theory but also commonality among theories. We call Ways derived from theories ‘Way-knowledge’. Such Ways can be applied to various instructional/learning designs if the supposed situation is matched. This study aims to support designers blending theories into their own instructional/learning design by

40

Y. Hayashi et al. / Ontological Modeling Approach to Blending Theories

providing an engineering infrastructure for accumulating and sharing variety of Way-knowledge derived from theories.

2. An example of comprehensive model of educational theories In our ontological modeling framework for education with the L/I/ID ontology, educational theories are modeled as Way-knowledge. In this section, we will discuss how Way-knowledge describes educational theories comprehensively. Fig. 2 shows an example of an I_L event decomposition tree. This tree represents a decomposition of the I_L event, “Facilitate learning / Knowledge state”. An oval node represents I_L event. Its label expresses combination of instructional action and change of learner state in the form of “instructional action/change of learner state”. The links between I_L events found below and above a square represent a Way. I_L events joined to a square and located below it represent an AND relationship. This means that, according to a Way, an upper I_L event is achieved when all of the lower I_L events are achieved. If more than one Way leads to an I_L event, it represents OR relationship among Ways, that is to say, there are more than one method to achieve the change of learner state. This relationship provides alternatives to make more detailed instructional/learning design for designers. Such an I_L event decomposition tree with OR relationship is called “General I_L event decomposition tree”. The general I_L event decomposition tree shown in Fig. 2 covers the whole process of instruction/learning - from preinstruction to assessment -. The foundation of this process is the five major learning components by Dick et al [5]. They are a summary of Gagne’s nine events of instruction [7]. These theories are basically considered to be based on cognitivism approach but to be somewhat eclectic in view of paradigms - behaviorism, cognitivism and constructivism - [17]. This model is intended to be a comprehensive model of instructional/learning theories by giving a shape to Dick’s components based on various theories in different paradigms. Now, we take a close look at the decomposition below the I_L event, “Exercise the learner / Absorbed (Fig. 2 (B))”, because this decomposition includes both cognitivism and constructivism approaches with OR relationship of Ways. Ways included in this decomposition are based on cognitive approach - Gagne’s nine events of instruction [7] and Merrill’s component display theory [13] - and constructivist approach - Collins’s cognitive apprenticeship [4] and Jonassen’s model for designing constructivist learning environments [11] -. The point of selecting principles from multiple theories is the change of learner state. According to Carey [3] and Ertmer and Newby [6], aspects of constructivist approach can be compatible with aspects of prescriptive approach for specified types of learners and learning outcomes [5]. From this viewpoint, Dick relates constructivist strategy to the five major learning components. In the similar line of these thought, we proposes a modeling method for organizing any theories in terms of what and how their strategies intend to achieve. This study sets up the working hypothesis that the idea of states in the learning process is common while the assumed mechanism of developing knowledge is different for each paradigm 1 . With this consideration, we have organized learner states intended to be shared among different educational theories. “Being ready to learn (Fig.2 (A))” and “Self-aware (Fig.2 (D))” are examples of such learning states. Making attempts to organize common concepts and to interpret (supposed) intention of theories as the change of learner state will afford a better understanding and utilization of educational theories. 1

Note that we are not saying all the learning theories share the same learning states.

Y. Hayashi et al. / Ontological Modeling Approach to Blending Theories

41

Let us examine blending of these approaches in more detail. The I_L event “Exercise the learner / Absorbed (Fig. 2 (B))” is decomposed into two sub-I_L events: “Eliciting performance / Absorbed (Fig. 2 (C))” and “Providing informative feedback / Self-aware (Fig. 2 (D))”. This decomposition is derived from Gagne’s events. To put it briefly in terms of what to be achieved, the former objective is to develop what is learned through practice and the latter one is to become self-aware in order to make the practice more efficient. These I_L events can be decomposed by both of cognitivist and constructivist approaches. For example, there are two Ways to decompose “Provide informative feedback / Self-aware”. Cognitivist approach (Fig. 2 (10)) gives relatively explanatory feedback, e.g. giving correct answer or help. On the other hand, constructivist approach (Fig. 2 (11)) gives relatively inquiring feedback, e.g. providing hint or assisting learners to articulate. Constructivist approach also has other characteristics such as authenticity and self-construction of knowledge, though this is not shown in Fig. 2 because of presenting a contrast to explanatory methods. These characteristics can be described in a more detailed model but are too complicated to explain in detail here. The key point of the blending of these approaches is, as discussed in the previous section, to take particular note of the change of learner state. These theories indeed have differences in the method. However, at the same time, the objective (intended change of learner state) can be described as common in becoming self-aware in this example. From this viewpoint, we set both the cognitivist and constructivist approaches as a Way to decompose “Providing informative feedback / Self-aware (Fig. 2 (D))”. In the same manner as this, other cognitivist and constructivist methods are also organized as Way knowledge shown in Fig. 2 (1) – (13).

3. Toward a theory-aware instructional/learning design support system One of the remarkable applications of our ontological modeling framework for education is theory-aware design support system for learning contents [8]. By “Theory-aware” [14], we mean the capability of information systems which can support the activities of users based on the understanding of relevant theories. Nkambou et al. [15] discuss the benefits of accessing theories and required functionalities of theory-aware ITS authoring environment. The benefits are: 1) make decisions (macro, micro) after reflection and reasoning, 2) communicate about or explain their design decisions, 3) check consistency among design decisions, intra-theory and inter-theories, 4) produce scrutable learning environments, 5) have heuristic knowledge grounded in theoretical knowledge. The required functionalities are that authors can: 1) ask the system what theories apply best to this or that learning situation/goal, 2) ask the system to show examples, 3) ask the system for advice on whether this element of a theory can be combined to an element from another theory, what is the risk associated to doing so, any preferable other solution, etc. Referring to their argument, this study focuses on the following two points as the requirement for realization of theory-awareness in an instructional/learning design support system: The system can (1) help designers to build theoretically valid model of learning/instruction, and (2) explain the help with the theoretical justification to designers. We now discuss how our ontological modeling framework for education contributes to these two advantages. As discussed in the previous sections, learning/instructional strategies included in educational theories are described as Way-knowledge in our modeling framework. Way-knowledge is a relationship between macro-I_L event and some micro ones with desired change of learner state (goal) and necessary condition for the change (situation). In

42

Y. Hayashi et al. / Ontological Modeling Approach to Blending Theories

addition, Way-knowledge is described with not each theory’s own terminology but our L/I/ID ontology. This ontology is designed to accept terminological difference among educational theories and paradigms on a conceptual level as mentioned before. Based on this ontology, Way-knowledge is not only human-readable but also machine-readable. This enables systems to expound educational theories described as Way-knowledge and to interpret models of learning/instruction built by learning/ instructional designers founded on educational theories. These will be quite helpful to learning/instructional designers for in-depth understanding of theories and for communicating about their design decisions and products. Another characteristic of our modeling framework is that a theory is described as a set of Way-knowledge. In other words, a theory is split into pieces of strategy and each strategy is described as a Way-knowledge. For example, in fig. 2, Way-knowledge (1), (2), (4), and (6) is derived from Gagne and Briggs’s theory, and (8) and (12) derives from Merrill’s component display theory. Such modeling of theories helps learning/instructional designers to make a model of learning/instruction from various viewpoints such as those that follow. One viewpoint is that a support system can pick and choose applicable strategies within a theory according to the grain size of process. If a designer wants to decompose complete learning/instructional process by him/herself, Way knowledge (1) is recommended. On the other hand, if he/she wants to do more detailed process, Way knowledge (4) or (6) are recommended. The other is that a support system can also suggest whether an element of a process derived from a theory can be exchanged by an element derived from another theory. Way-knowledge (10) and (11) illustrated below the I_L event “Provide informative feedback /Self-aware (Fig. 2 (D))” is a good example to illustrate the possibility of exchange. Such guidance becomes possible with inter-theory comparison in terms of strategies included in theories or derived from them.

4. Conclusion We have discussed a modeling framework for learning/instructional design based on ontological engineering. The characteristics of this framework include: 1) a theory/ paradigm-independent ontology for modeling learning/instruction, 2) compatibility between prescriptive and descriptive models derived from educational theories, 3) theory-awareness brought out by an ontological modeling framework. In this paper, we have concentrated on how the theory/paradigm-independent ontology contributes to modeling learning/instruction from a comprehensive viewpoint of various educational theories. Our modeling framework based on this ontology will be helpful to blend of educational theories as discussed in section 2, and to enhance the quality of support by information systems for learning/instructional design as discussed in section 3. The following issues to be examined further still remain: 1) investigating the theory/paradigm-independence of the L/I/ID ontology through organizing much more educational theories in our modeling framework, 2) considering the relation between learning objects and abstract design of learning contents described as an I_L event decomposition tree, 3) implementation of a theory-aware design support system for learning contents, and 4) compliance with standards for semantic web and e-learning.

Y. Hayashi et al. / Ontological Modeling Approach to Blending Theories

43

Acknowledgments We would like to thank to Dr. Daniele Allard for her helpful comments. References [1] Bourdeau, J. and Mizoguchi, R.: “Collaborative Ontological Engineering of Instructional Design Knowledge for an ITS”, Proc. of ITS2002, pp.399-409, 2002. [2] Bourdeau, J. and Mizoguchi, R.: “Selecting theories in an ontology-based ITS authoring environment”, Proc. of ITS’2004, pp. 150-161, 2004. [3] Carey, J. O.: “Library skills, information skills, and information literacy: Implications for teaching and learning”, School Library Media Quarterly Online, Vol. 1, 1998 Available: http://www.ala.org/aasl/SLMQ/skills.html [4] Collins, A., Brown, J. S., and Newman, S. E.: “Cognitive apprenticeship: Teaching the crafts of reading, writing and mathematics”, In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser. Hillsdale, NJ: Lawrence Erlbaum Associates, pp. 453-494, 1989. [5] Dick, W., Carey, L., and Carey, J. O.: The systematic design of instruction, Sixth edition, Addison-Wesley Educational Publisher Inc., 2004. [6] Ertmer, P. A., and Newby, T. J.: “Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective”, Performance Improvement Quarterly, 6 (4), 50-70, 1993. [7] Gagne, R. M. and Briggs, L. J.: Principles of Instructional Design (2nd Ed.). Holt, Rinehart and Winston, New York, 1979. [8] Hayashi, Y., Bourdeau, J. and Mizoguchi, R.: “Ontological Support for a Theory-Eclectic Approach to Instructional and Learning Design”, Proc. of EC-TEL 2006, (to appear), 2006. [9] IEEE LTSC, The Learning Object Metadata standard. Available: http://ieeeltsc.org/wg12LOM/lomDescription [10] IMS Global Learning Consortium, Inc.: IMS Learning Design. Version 1.0 Final Specification, 2003. Available: http://www.imsglobal.org/learningdesign/ [11] Jonassen, D.: Designing constructivist learning environment, In Reigeluth, C. M. (Eds.): Instructional-design theories and models A new paradigm of instructional theory, Mahwah, New Jersey: Lawrence Erlbaum Associates, Inc., pp. 215-239, 1999. [12] Kitamura, Y., Kashiwase, M., Fuse, M., Mizoguchi, R.: “Deployment of an Ontological Framework of Functional Design Knowledge”, Advanced Engineering Informatics, Vol. 18, Issue 2, pp. 115-127, 2004. [13] Merrill: “Component display theory”, In Reigeluth, C. M. (Ed.), Instructional-design theories and models: An overview of their current status. Hillsdale, New Jersey: Lawrence Erlbaum Associates, Inc., pp. 279-333, 1983. [14] Mizoguchi, R. and Bourdeau, J.: “Using Ontological Engineering to Overcome Common AI-ED Problems”, International Journal of Artificial Intelligence in Education, Vol.11, No.2, pp.107-121, 2000. [15] Nkambou, R., Frasson, C., and Gauthier, G.: “CREAM-Tools : An Authoring Environment for Knowledge Engineering in Intelligent Tutoring Systems”. In: Murray, T., Blessing, S. and Ainsworth, S. (Eds): Authoring Tools for Advanced Technology Learning Environments: Toward cost-effective adaptative, interactive, and intelligent educational software. pp. 93-138. Kluwer Publishers, 2003. [16] Psyche, V., Bourdeau, J., Nkambou, R., and Mizoguchi, R.: “Making Learning Design Standards Work with an Ontology of Educational Theories”, Proc. of AIED2005, pp. 539-546, 2005. [17] Snelbecker, G. E.: “Is instructional theory alive and well ?”, In Reigeluth, C. M. (Ed.), Instructional-design theories and models: An overview of their current status. Hillsdale, New Jersey: Lawrence Erlbaum Associates, Inc., pp. 437-472, 1983. [18] Reigeluth, C. M.: “Instructional-design: What is it and why is it?” In Reigeluth, C. M. (Ed.), Instructional-design theories and models: An overview of their current status. Hillsdale, New Jersey: Lawrence Erlbaum Associates, Inc., 1983.

44

Neutral (1)

Neutral

Constructivism

(2)

Cognitivism

Neutral

Constructivism (4)

(5)

(3)

(B)

(6)

(C)

Constructivism (D)

(7)

Cognitivism

Constructivism

Constructivism Cognitivism (8)

(9)

(11)

(10)

Constructivism Cognitivism (12)

Fig. 2 A general function decomposition tree

(13)

Y. Hayashi et al. / Ontological Modeling Approach to Blending Theories

(A)

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

45

A Case of Blending Learning in Computer Teaching üüThe Model and the Application a

Li Cuiling a, Wang Hong b, Zhang Huiyu a School of Computer and Information Technology, Beijing Jiaotong University, China b Higher Education Press, China [email protected]

Abstract: In this paper, firstly we introduce four key elements of application of blending learning in teaching, Dnd then we talk about the application of blending learning in the field of computer teaching based on the practical case of curriculum of ‘Fundamentals of Computer for University’. Keywords: Blending Learning, model, application

1. What is Blending Learning? Simply put, blending learning can be described as a learning mode that mixes different delivery modalities with the objective of combining the advantage of traditional learning method and that of e-learning. That is, the instructor should lead, enlighten and watch the learning course, and also inspire students of initiative, enthusiasm and creativity during the learning course.

2. The Model --Four Radical Elements of a Blending Learning Process Based on the experience of deploying blending learning and the control over the quality of the process, four key ingredients emerge as follows: x Arrange before class. x Strategy analysis. x Performance support service. x Evaluations.

3. The Application of a Case of Blending Learning The curriculum of ‘Fundamental of Computer for University’ is made up of a web station, books, e-learning materials , etc. The web is made up of ‘Curriculum Summary’, ‘Curriculum Content’, ‘Exercises’ and ‘Schoolwork’, ‘Courseware’, ‘Practical Teaching’, and ‘Test Online’, etc. It is the platform to implement the network teaching.

46

L. Cuiling et al. / A Case of Blending Learning in Computer Teaching

3.1Preparation before class 3.1.1 Identifying the instruction goals and summary Adding the summary before the learning content can make students understand the whole learning target, and at the mean time help teachers control the learning process and pace of learning. 3.1.2 Analyzing the learners The objects of the curriculum are freshmen, and the starting levels vary widely with the differences among them in the aspect of the mastering of computer knowledge, just as the table shows.

percentage year 2002

Table 1 the Students’ level of Mastering Computer Knowledge Know some Know computer Online and Use Use simple before and can send e-mail Word Excel knowledge operate simply 43ˁ 32ˁ 23ˁ 28ˁ 18ˁ

2003

54ˁ

51ˁ

41ˁ

30ˁ

23ˁ

2004

66ˁ

62%

54ˁ

42ˁ

27ˁ

2005

72ˁ

71%

68ˁ

50ˁ

35ˁ

We found that the general level of the degree that students mastering computer knowledge increased as the time goes. It is very good for our network teaching, but we should recognize the differences among different areas and different individuals. 3.1.3 Dividing students into groups In order to carry out the process of study successfully, we divided them into groups. We considered of the elements such as the characteristics of students, original level of knowledge mastering and skills, and sex, etc. And also we encouraged them to study in cooperation. In this practice, we divided 35 students into 7 groups. We also let them vote one leader in each group. Communication inner groups and between groups were encouraged. In order to know the knowledge basis of the students, teachers conducted a pre-assessment by giving a corresponding test using the system of ‘test online’ on the web. 3.2 Implementing the process With the goals of training students of abilities of self-study, innovation and cooperation, we used the web of ‘Fundamentals of Computer for University’ and adopted different kinds of instruction strategies to develop the practice of teaching of computer for university based on the mode of blending learning. Therefore, we adopted 3 strategies as follows: class instruction, self-study and cooperative study. x Face-to-Face Instruction. According to the approach of classifying teaching results by Gagne, we classifyed instructional goals in the following domains: verbal

L. Cuiling et al. / A Case of Blending Learning in Computer Teaching

47

information, intellectual skill, psychomotor skill, and attitude. Different contents should be presented by different way, as follows:

clarifying verbal information intellectual skill psychomotor skill attitude

Table 2 Clarifying and Method of Learning Method for learning Face-to-face, self- learning, cooperative learning, etc Face-to-face, cooperative learning, discussion learning, exploring learning, research learning, etc Operation exercise, case learning cooperative learning, Face-to-face

x

Self-study. In the design of the curriculum, we considered adequately that we should train them to study by themselves, to analyze and solve problems by themselves. x Cooperative Learning. We have put forward the plan to divide students to small groups. During the learning course, small-group learning was feasible and very effective. Firstly, we assessed the former situation of groups, allowing the adjustment of group numbers and outer-group numbers, in order to avoid the disadvantage of the method of group that teachers decided. The next task was to divide the work to each number in a group. After inner-group communications, the group leader could complete this task. Then a discussion was followed to decide everyone’s task. Secondly, we chose the corresponding learning task, and developed the group learning using three different methods: Inner-group Communication, Inner-group Cooperation and Communications among Groups. At last, a simple assessment concluding an assessment to the whole fruit for every group and each member in a group was done. During the learning course, we used the tool of the web of ‘Fundamentals of Computer for University’ to develop cooperative learning, providing a new means. 3.3 Performance support materials During the practical teaching course, to provide students with good service and support for study guarantees the completion of the learning task. Considering of the characteristic of blending learning, we should do this as follows. x Providing Comprehensive Resources. It should contain teaching materials and learning materials. Supported by the web of ‘Fundamentals of computer for university’, it also should contain some digital resources, for example, powerpoint lectures and other complementary resources, etc. x Tutoring and Answering. We arranged face-to-face tutoring once a week. The teacher and teaching fellow were responsible to answer the questions, correct students’ assigned work, tutor their experiment and psychology, train them use the platform to study, and tutor their learning style. They also deployed discussion areas online to encourage students to communicate and study. x Holding lectures. Holding lectures could not only increase students’ knowledge, but also arouse their interests.

48

L. Cuiling et al. / A Case of Blending Learning in Computer Teaching

4. Evaluation A good mechanism of evaluation is a very useful support to teaching course. The evaluation of the mode of blending learning is classified into two categories: formative evaluation and summative evaluation, just as the following table shows. Table 3 Item, Content and Methods of Evaluation Content of evaluation Methods of evaluation Evaluation to students Usual behavior and the mastering Formative evaluation, degree of content and skills summative evaluation Evaluation to teachers The feasibility and effects of the Formative evaluation, instructional design summative evaluation Evaluation to learning The learning style and other Formative evaluation strategy elements during the learning course Item



Formative evaluation is the process of collecting data and information in order to improve the effectiveness instruction. It contains evaluation to the behavior and learning effects of students, the organization and service of teachers and various strategies that are adopted during the teaching activity. Its purpose is to find problems and solve them in time to guarantee the teaching effects. Summative evaluation is the process of collecting data and information in order to make decisions about the acquisition or continued use of some instruction. In the summative evaluation of this term, generally we adopted the form of final examination. Students’ usual behavior accounted for 60%, homework accounted for 20% and final exam accounted for 20%. Acknowledgments We thank the people, Wang Hong and Zhang Huiyu, who helped me a lot during the course.

References [1] Kekang He, 6ee the 1ew 'evelopment in the 7heory from %lending /earning. http://www.etc.edu.cn/academist/hkk/blending.htm. [2] Ronghuai Huang, Jinbao Zhang and Yan Dong,The Discussion about the Four Key Ingredients during the Course of Network Teaching. http://www.vschool.net.cn/elr/zhiliang/zl0015.htm. [3] McAteer, E., Tolmie, A., Du.y,C., Computer-mediated &ommunication as a /earning 5esource. Journal of Computer Associated Learning,(1997).

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

49

A Combined Method for Extracting Rules with Improved Quality ˄

1

Fuyan Liu ʳ Hangzhou Dianzi University, Hangzhou, P.R.China, 310008 [email protected]

Abstract: This paper presents an effective approach, which combines association rules and clustering algorithm for extracting rules on the web. The advantage of the method is that it can provide more precise and detailed rules compared with traditional algorithms. The paper analyzed some issues in previous approaches first. Then the proposed method is presented. Experiments are performed to evaluate the performance of the combined approach. Experimental results show that the proposed method is more effective and efficient. Keywords: Clustering, association, inference, user profile, confidence

Introduction Compared to traditional techniques, one distinguished capability of the web is the ability to perform continuous unobtrusive observations on user browsing behavior. A web server can collect the information on the decision process rather than only the decision result. Based on these data, we can capture a more comprehensive user profile, which can be used as foundation for serving users. Usually there are two types of user service: individual-based and group-based. Most previous studies focused on individual-based inference. However, to new users, the individual-based rules may not make sense, even to old users, may be still difficult to adapt to their changing preferences. Thus, it is better to use a group-based inference mechanism. In this paper some concepts such as user profile are introduced first. Then some issues in previous approaches are analyzed. In the following section an effective combined approach to infer rules with improved quality is proposed. In addition, experiments are performed to evaluate the performance of the proposed approach. The experimental results show that the combined method can provide more effective and efficient performance.

1. Some Concepts There are different views about the user profile, for example, paper [1] defined the user profile as a set of keywords, which describes the information the user is interested in; paper [2] defined the user profile as user’s likes and dislikes. We adopt a broader view of the user profile as a database, which maintains all information about users [3]. For information from inside the web site, one source is provided by customers on purpose, such as data from registration form, online questionnaire, online orders, and message board, etc. These data can be collected through CGI or Cookies. Another way to collect information from inside the web site is to observe users’ behavior without the users’ awareness, e.g. online browsing behavior can be collected by ASP, Cookies or from the log files of the server. Information from outside the web site might be collected by

50

F. Liu / A Combined Method for Extracting Rules with Improved Quality

search engine from virtual community such as bbs, newsgroups or personal homepage, etc. User data can be divided into raw data and refined data. Raw data is the original data, such as a user’s ID, age, sex, browsing behavior and purchasing records. Refined data refers to data such as usage status, habit, affinity group, and preference evaluation, etc. It is extracted, hybrid or inferred from raw data on purpose to have a better description of users. Refined data can consist of individual related data as well as group related data. It is better to classify inference foundations into two types: individual-based and group-based. The individual-based inference is based on individual data. The group-based inference is based on affinity group data. Both can be applied to infer a user profile as well as association rules. Our paper will focus on group-based inference.

2. Considerations of the Proposed Method Most of previous studies focused on individual-based inference. There are some unique problems that stem from the special characteristics when neural networks or induction learning techniques are used, e.g. a stochastic problem or large number of attributes, infinite number of states, low response rate, and difficulty in explaining results etc. may be faced. Therefore, in this paper we use association rules instead. An association rule is an expression: XŸY, where XI, YI, XˆY= I, and I={il, i2,..., im} is a set of items. Association rules can avoid the stochastic problem, since they calculate the proportion of the specific item sets in the database to identify rules rather than learn from each user [4]. Also association rules have no problems about large number of attributes and infinite number of states because the analysis unit is item sets such as transactions or browsing on the web not user’s attributes. Besides, the low response rate does not cause much trouble because browsing data can be collected from the web. In order to find association rules Apriori is the most essential algorithm. But the algorithm only explores the association between items. It is important to consider categories of different subjects in the data set. So we use Algorithm Basic, which takes account of categories to explore association rules [5]. Previous studies explore association rules based on all transactions, where they didn’t consider the concept of segmentation. Thus the extracted association rules are too general to reflect different user’s needs. But users of different affinity groups have different desires, geographical locations, preference and behavior, etc. It is better to cluster all users into different groups before exploring association rules. The main goal of cluster analysis is to identify clusters existing in the data. There are many clustering algorithms developed for different applications and k-medoid methods are more popular due to their robustness, efficiency and being independent of the order in which the objects are selected during operations. Among k-medoid methods, PAM is a simpler and well-accepted one [6]. However, PAM works quite well only for small data sets. Its computing complexity is O(k(n-k))2 for one iteration. When the number of objects n and the number of clusters k are quite large, then it is too costly. In this paper we adopt CLARA for clustering [7]. Unlike PAM which will deal with the entire data set, CLARA draws a sample of the data set first, then applies PAM on the sample. Its computing complexity is O(ks2 +k(n-k)), where s is the sample size and it is quite smaller than n.

3. Framework of the Method We propose a combined method to infer association rules, which is shown in Fig.1. Clustering is implemented first, which is based on user’s demographic, geographic, and

F. Liu / A Combined Method for Extracting Rules with Improved Quality

51

behavioral data stored in the raw data, while using CLARA to identify the affinity group. The k-medoids and the individual user clusters are recorded in the refined data set of the user profile for the purpose of identifying the affinity groups that users belong to. Then for each cluster, Algorithm Basic is used to Inference engine learn the associations of relevant subjects Association Clustering such as browsing patterns and transactions rule algorithm algorithm to find association rules, which are valid for (A-BASIC) (CLARA) a particular cluster only. These rules retrieve transactions or browsing patterns from the raw data set. For the description of Output Row Site site contents and advertisements, it retrieves Refined rule base related data from the site database. The data set data set database User profile results of group-based association rules are stored in output rule base. Fig.1. Diagram of the combined method 4. Experiments and Conclusions In order to evaluate the proposed method, experiments were performed. In the experiments, a user profile was built first by the method and then the performance of the method was evaluated. The data used are collected from experiments of online assignment in the course of the Management Information Systems (MIS), which involves five categories: Fundamental Techniques (FT), Strategic Planning and Development Approaches (SP&DA), System Analysis (SA), System Design (SD) and System Implementation (SI). There are 198 students of five classes participating in the experiments over a period of two weeks in our computer lab. The experiments began with a registration form that required students to fill in their personal data and then submit to the lab server. We also used log files in the lab server to collect browsing behaviors of students during the specified period. In the experiments, students are firstly classified by CLARA of a k-medoid method before exploring the association rules. The collected students’ demographic and behavioral data are used for clustering. The registration form is used to collect students’ sex, age, class, daily hours of browsing, daily hours of online doing assignment, date of finishing assignment and total hours spent on assignment. It is assumed that the students may fall into one of the three groups according to our experience and observation: Ɣ Group 1: They visit the web site regularly, almost every day, and spend more time on the assignment during the first week of the specified period and they finish the assignment in advance. Ɣ Group 2: They visit the web site randomly and spend less time on the assignment during the first week of the specified period. But they spend much more time during the second week and finish the assignment on time. Ɣ Group 3: They visit the web site randomly but almost do nothing on the assignment each day. And they rush into the assignment on the last one or two days during the specified period and can not finish assignment on time. As stated earlier, in this paper we use CLARA algorithm to perform clustering. For accuracy, the quality of the clustering is measured by the average dissimilarity of all objects in the entire data set, although CLARA draws a sample of the data set. Then we apply PAM on the sample. Here we set the number of samples to 3 and set the size of each sample to 60. The predefined 3 clusters are classified as shown in Table 1. It can be seen from Table 1 that cluster 1 has more female students and above 89.7% students of cluster 1 visited the web regularly every day and finished the assignment in

52

F. Liu / A Combined Method for Extracting Rules with Improved Quality

advance. Above a half of students spent less than 40 hours for finishing the assignment. The other two clusters in Table 1 can be analyzed similarly. After clustering, the association rules for browsing behavior were extracted with Algorithm Basic based on all students with and without clustering. As a result, the numbers of association rules are 55 and 13 respectively and the group-based approach can provide more detailed rules. Table 1. Result of clustering Cluster attributes Sex Age Total hours spent on assignment Date of finishing assignment Daily online hours

Male Female Average ”30 hours 30~40 hours >40 hours In advance On the last day Delay Regularly Randomly

Cluster 1 10.6% 13.6% 21.4 5.1% 7.6% 11.6% 24.2% 0.0% 0.0% 21.7% 2.5%

Cluster 2 35.9% 24.7% 22.6 0.0% 0.0% 60.6% 35.4% 25.3% 0.00% 25.3% 35.4%

Cluster 3 9.1% 6.1% 20.9 14.1% 1.1% 0.0% 0.0% 0.0% 15.2% 0.0% 15.2%

Now we compare the performance using the association rules and two measures: support and confidence for the non-clustering entire data set and clusters separately. Table 2 shows the comparison of clusters with the non-clustering entire data set for each measure and each association rule. All rules in cluster 1, cluster 2 and cluster 3 have higher support and higher confidence than that of the non-clustering data set. Table 2. Measure comparison on browsing NonClustering Clustering Cluster 1 Cluster 2 Cluster 3 Association rules S C S C S C S C (C1,C2) 0.13 0.35 0.33 0.49 0.28 0.45 0.15 0.38 (C2,C3) 0.15 0.38 0.40 0.52 0.34 0.48 0.17 0.40 (C3,C4) 0.15 0.40 0.43 0.54 0.30 0.56 0.16 0.43 (A,C4) 0.12 0.26 0.18 0.31 0.20 0.32 0.12 0.28 (A,C5) 0.11 0.22 0.15 0.28 0.16 0.28 0.12 0.22 Note: (1) the value of support is set to 0.1; S, C represent degrees of support and confidence respectively. (2) A and C1, C2, C3, C4, C5 represent the assignment and five categories: FT, SP&DA, SA, SD, SI.

In this paper a combined approach is proposed to infer rules of user browsing on the web, which combines clustering and association rules to avoid the troubles such as the stochastic problem, the interpretation difficulty, too many attributes and a low response rate etc. Experimental results show that the combined approach increases the degree of both support and confidence of association rules and it confirms that the proposed approach can produce more detailed and precise rules.

References [1] John Davies, Richard Weeks, Mike Revett. Jasper: Communicating information agents for WWW. http:// www.w3.org/Conferences/WWW4/Papers/180/. [2] George Leaks, George M. Giggles (2004) A Lifestyle-based approach for delivering personalized advertisements in digital interactive television. Journal of Computer-Mediated Communication, 9, 2, 23-45. [3] Lai H., and Yang, T. C. (1998) A system architecture of intelligent-guided browsing on the Web. Proceedings of the Thirty-First Annual Hawaii International Conference on System Sciences, 4, 423-432. [4] Hsiangchu Lai and Tzyy-Ching Yang (2000) A group-based inference approach to customized marketing on the Web-integrating clustering and association rules techniques. Proceedings of the 33rd Hawaii International Conference on System Sciences, 6054-6063. [5] Shi Zhong-zhi (2002) Knowledge Discovery. Tsinghua University Press, Beijing. [6] Carey V. J, Kollekolle. http://www.biostat.ku. dk/~pd/bioC-2003/vclec4.pdf. [7] Shu-Chuan Chu, John F. Roddick, Tsong-Yi Chen and Jeng-Shyang Pan (2002) Efficient search approaches for K-medoids-based algorithms. Proceedings of TENCON '02, 712-715.

Programming

This page intentionally left blank

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

55

The Impact of CABLE on Teaching Computer Programming Ioana CHAN MOW, Wing K. AU & Gregory C. R. YATES Dean of Science, National University of Samoa Program Director & Senior Lecturer, University of South Australia Senior Lecturer, University of South Australia [email protected] Abstract: This paper reports the second of two studies on the impact of a Cognitive Apprenticeship-Based Learning Environment (CABLE) in the teaching of computer programming. The pedagogical model used in this study employs a combination of instructional strategies including directive support, responsive cognitive apprenticeship, collaborative learning, stimulating metacognition, using technologies via the use of teleapprenticeship and online discussion. In an earlier study, students who participated within the CABLE project scored more highly on test scores, relative to comparable students who did not participate within CABLE, but these effects were found to be restricted to highability students. In the present study, students who participated within CABLE scored more highly than those participating within the non-CABLE group. However, with an enhanced CABLE environment, the benefits of CABLE were now evident in both ability groups, with the effects being more prominent within the low-ability group. Keywords: Cognitive metacognition

apprenticeship,

collaborative

learning,

tele-apprenticeship,

1 Introduction Computer programming is a difficult and challenging subject area which places a heavy cognitive load on students [1, 2]. After two years of learning programming, most novice programmers are still struggling to be proficient [3, 2]. From an examination of current research in this field, it can be postulated, that one reason computer programming instruction seldom results in the successful transfer of problem solving skills, lies in a lack of understanding about good instructional approaches in this direction [4, 5]. This paper reports the second of two studies on the impact of a Cognitive Apprenticeship-Based Learning Environment (CABLE) in the teaching of computer programming that would promote problem-solving skills of university students in Samoa. The main instructional approach used in this study is described as cognitive apprenticeship. The notion of apprenticeship stems from Vygotskian psychology, but we based our approach after the work of cognitive psychologists such as Collins, Brown and Newman [6] and others. Cognitive apprenticeship places emphasis upon reflective thinking as a metacognitive experience. The approach encourages metacognitive thinking, the use of directed teacher instruction to guide problem solving, and the use of scaffolding. The pedagogical model researched in the two studies represents our attempt to devise a viable instructional model based around the construct of apprenticeship. The aim was to achieve this goal through using a combination of instructional strategies working in concert to

56

I. Chan Mow et al. / The Impact of CABLE on Teaching Computer Programming

provide the learners with a highly demanding but responsive instructional experience that differed from those derived within more traditional learning environments. Cognitive apprenticeship is a model of instruction that involves the effective communication of domain knowledge in such a way that the students become aware of the thought processes involved in knowledge construction within that domain. As an instructional approach it is directed at teaching processes that experts use to handle complex tasks, characterized by a number of teaching methods [6]. The first of these, is modelling where the teacher models his or her thought processes in solving problems within a domain. The second of these methods is guided practice or coaching where the student attempts to solve the problems for themselves with the support of the teacher to answer specific queries. A third method is scaffolding, where the teacher assists students to manage complex task performance and then gradually withdraws support from the student (fading). Other key components of this approach include articulation, where the student attempts to articulate their problem solving strategies; reflection, where the students are encouraged to reflect on how they approached tasks and solved problems, possibly by discussion with other students and, finally, exploration which is intended to encourage learner autonomy and problem formulation by students. Another instructional strategy that is gaining prominence as an effective teaching method and is integrated into the learning environment being trialled is collaborative learning. There are many approaches to collaborative learning but all have the following characteristics in common [7]. It is a learning activity suitable for group work; it is small group based (usually 2-5); it has tasks which encourage cooperative behaviour; it is characterized by student interdependence; individual student accountability and responsibility for task completion. Our starting assumption is that the computer can be used as a tool assisting in both cognitive apprenticeship and collaborative learning. An important element of the CABLE learning environment is its use of computer-mediated communication techniques for implementing aspects of cognitive apprenticeship such as scaffolding, coaching, feedback and modelling. Computer-mediated communication refers to communication using a computer. Examples of computer-mediated communication include email, computer conferencing and electronic bulletin boards. How these techniques are utilized to support the learning process is referred to as electronic apprenticeship or tele-apprenticeship. Teleapprenticeship or online apprenticeship refers to the use of computer mediated communication techniques, for the implementation of cognitive apprenticeship [8]. Computer mediated communication techniques used in implementing tele-apprenticeship in the current study include email, online notes, interactive tests and a bulletin board. As mentioned earlier, the term used to refer to this pedagogical model, is the cognitive apprenticeship-based learning environment or CABLE, because although it is based on the cognitive apprenticeship approach, the approach used in this study, is further enhanced by the incorporation of elements of metacognition and collaborative learning, instructional elements, which studies have proven to be conducive to effective learning. Furthermore some elements of this cognitive apprenticeship based approach will be implemented electronically by means of email, bulletin board, online notes and interactive quizzes. This is referred to as tele-apprenticeship and a subsidiary aim of this research was to investigate the impact of computer-mediated communication in enhancing student learning. CABLE is a hybrid learning environment and it is implemented both in face-toface and online mode. This paper reports the findings of our second study on the impact of CABLE. In the initial study, results showed that students who participated in CABLE scored more highly on a post test measure of mastery of Java programming, relative to comparable

I. Chan Mow et al. / The Impact of CABLE on Teaching Computer Programming

57

students who did not participate within CABLE. However, these effects were found to be restricted to high-ability students. These results were disappointing in that the hope was to develop instructional procedures that would provide a richly motivating and responsive educational context that would appeal to low-ability students. One goal of this program lies within reducing variance within student achievement levels. From these findings our team re-evaluated our procedures and modified CABLE to include more scaffolding and more online resources. The effectiveness of this enhanced CABLE model is evaluated in the present study.

2 Methodology 2.1 Research design

Students participating in University Foundation Computer Studies class at the National University of Samoa were allocated to either the CABLE treatment or were taught in accordance with the traditional university model of teaching (i.e. Non-CABLE methods). This division was achieved through students being enrolled within different class times. Although the initial total enrolment was 80 students, complete data were able to be collected from 39 students within the CABLE group, and 33 students from the traditional group. Participants were students within their first year of studies from the Foundation (64%) and non-Foundation (36%) programs. The Foundation program was for students who entered at a higher GPA and were preparing for studies in overseas universities whereas the non-Foundation program was for those who entered at a lower GPA and intending to pursue study locally. The approximate average age of the participants was 19 years. Student records were available from previous computing courses in the form of test scores. The design of the present study was very similar to the first study. After six weeks of exposure to the treatments, all students completed the final test paper, and also completed a questionnaire intended to tap into their evaluations of their course experience. The post-test consisted of six questions which evaluated their knowledge of Java commands and also practical questions which tested the application of these Java skills and knowledge to solving a problem. Students were given program codes to explain, to troubleshoot and to predict program output. Furthermore, the assumption made here was that computer programming was a form of problem solving and hence these scores represented the problem-solving ability of these students. The questionnaire consisted of Likert items which gauged students’ attitudes towards the learning environment, effectiveness of feedback, effectiveness of collaborative learning, the effects on self-confidence, and students’ love of learning. A diary of students’ interviews was kept in order to provide some qualitative, narrative and descriptive data on the study. It is important to point out that there was considerable overlap in the instructional approaches in the two treatments. Students in both the cognitive apprenticeship and traditional groups were given the same set of notes and exercises on JAVA programming. Both groups were exposed to elements of the cognitive apprenticeship based approach such as feedback and coaching. In both groups, the teacher modelled computer programming theory and JAVA programming concepts using worked examples and reallife examples. A main difference between the two groups was in the provision of feedback to the students. In the control group, as was characteristic of didactic instruction, feedback was

58

I. Chan Mow et al. / The Impact of CABLE on Teaching Computer Programming

student initiated. With the experimental group, feedback was more structured and was given on a weekly basis. Feedback was provided by an online system where the lecturer provided individualised feedback via individualised emails sent to and received from each student. On a weekly basis, the students were expected to send an email to the lecturer, answering several questions. The first question required them to describe what activities and topics they had done during the week, and to indicate how they felt about their progress. The second and third question required them to describe any areas they are having problems with. The last question required the student to reflect upon what they have learnt and how useful they thought what they had learnt would be to them. The lecturer would then respond to each student via email by way of feedback and encouragement. From the individualised feedback, the lecturer could gauge areas most students were having problems with and then use their information to post some frequently asked questions (FAQs) and solutions on the class web-site, providing further feedback and guidance to students in the class. It was assumed that this feedback system would provide students with the opportunity to articulate their thoughts and ideas and also to reflect upon their work and their progress in class. A second differentiating factor between the two groups was metacognition. A key component of the cognitive apprenticeship based approach was the provision of a rich metacognitive experience to the learner, thus facilitating them to learn. This was facilitated within the cognitive group by encouraging students to reflect on their progress, problems encountered, what they had learnt, the usefulness of what they had learnt and also by the articulation of their thinking processes in the form of “think-alouds”. The third differentiating factor between the two groups was the incorporation of elements of collaborative learning. Within the cognitive apprenticeship based group, coaching and mediation would also be provided by a more capable peer as the students were be paired, with the more capable student collaborating with the weaker student in carrying out their programming tasks in class. 3 Results and Discussion 3.1 Achievement Test Scores The two treatment groups were found to be similar on non-Java prior test scores from earlier completed course units but diverged on the Java post-test scores. Results showed that there was an effect for CABLE treatment on post-test scores. That is, people who participated in CABLE scored more highly on the Java post-test than those who were in the traditional treatment. Statistical ANOVA procedures were used and showed a significant overall effect for treatment, F (1,53) = 8.48, p = .005. For further investigation, the two treatment groups were then split into two groups on the basis of the prior test scores. The prior test scores were taken as a measure of ability level. The median value of 50 was used, to generate a classification of high-ability and low-ability students. From an inspection of the data from the two ability levels separately, it was apparent that although the CABLE treatment had benefited both ability groups, the effect was more prominent in the less able group. The treatment effect was stronger in the less able group, with the F ratio increasing to 9.13 (p =.006). Inspection of effect-sizes (Cohen’s procedure) yielded d = 1.22 for low-ability and d = 0.66 for the more able group [The coefficient d is expressed in standard deviation units]. In other words, although the CABLE treatment benefited both ability groups, the effect was stronger for the less able group, as indexed upon through their prior achievement scores. This interaction effect is shown in Figure 1

I. Chan Mow et al. / The Impact of CABLE on Teaching Computer Programming

59

18

16

Java Posttest Means

14

12

TREAT

10

Trad 8

CABLE Low Ability

High Ability

ᴾ

Figure 1. Mean scores on post-test for students of low and high ability

3.2 Questionnaire responses A measure of general course affect (or ‘liking’) was generated through summing the responses to 11 items on the questionnaire. It was found that on this measure, the two treatment groups exhibited similar levels. Out of a possible score of 44, the actual mean was 34.1 (SD of 3.96), which indicated a very high level of course approval (Note: the natural midpoint on the scale was 27.5). In short, students from both treatment groups evidenced very high levels of liking for their course experience. The levels of course affect was found to be independent of either treatment mode or ability status. ᴾ

3.2.1 Responses to the Unstructured questions Responses to Question xviii: Probe: Problems encountered (if any) during treatment

For the traditional treatment, 6 out of 24 students reported having no problems in using this learning mode. The main problem students identified was the difficulty in understanding Java programming (12 out of 24 students). Other problems reported included (a) not enough time to do work, (b) difficulty in accessing the lecturer, and (c) difficulty in specific Java concepts such as declaring types, running the program. In the CABLE treatment, 4 out of 24 students reported having no problems with the learning environment. As in the traditional group the main problems students reported were (a) difficulty in understanding Java, and (b) too many Java terms to learn. Responses to Question xix Probe: List reasons why you think this is an effective or ineffective form of learning environment

Fifteen out of 24 students in the traditional treatment agreed it was an effective learning mode. The following reasons were given: (a) it kept students alert, occupied and provided experience of working under pressure, (b) it prepared students for programming independent of the lecturer, (c) improved student understanding of Java, (d) the provision

60

I. Chan Mow et al. / The Impact of CABLE on Teaching Computer Programming

of a balance of both practical and theory improved understanding of Java, (e) provided encouragement to learn, and (f) provided more computing knowledge and skills. Five students found the learning environment ineffective. One claimed it was because it was the first time they had studied computers, while another resented disruptions from other students. For the CABLE treatment, 15 of 24 students reported the learning mode as effective. The main reasons given were (a) the use of both practicals and lectures facilitated a better understanding of Java, (b) increased motivation and improved confidence in learning Java, (c) improved understanding of Java and, (d) the use of email. Two students found the learning environment ineffective, one of them giving the reason as insufficient coverage time for topics. 3.2.2 Effectiveness of Online learning To gauge the effectiveness of online learning, three data sources were used, (a) the responses to the questionnaire questions 12 – 17 administered only during the cognitive apprenticeship based treatment, (b) responses from personal student interviews, conducted in weeks 6 and weeks 12 of the study, and (c) an analysis of products, processes and perceptions based on the Triple Framework approach for evaluating online environments [9]. Questionnaire questions and interviews with students focused on four main issues: (a) The effectiveness of email for feedback (b) The effectiveness of online notes on class website (c) The usefulness of posting sample test solutions online (d) Whether students like working in pairs (a) The effectiveness of email for feedback The majority of the students interviewed agreed on the usefulness and effectiveness of email as a means of feedback. The main source of frustration was technical problems preventing effective access to email. Most of the students regarded email as very helpful as it gave useful and immediate feedback. (b) The effectiveness of online notes on class website All of the students interviewed liked online notes, giving the main reason for liking, as the ease of access and also that they could access the online notes at any time. Students also found the interactive self tests useful as a means of reviewing before the Java test. Again, the main complaints were technical. (c) The usefulness of posting sample test solutions online All of the students interviewed liked the idea of sample solutions online as they said they could i) access them any time, ii) useful for revision and iii) were useful for doing test corrections. (d) Whether students liked working in pairs All of the interviewees liked working in groups as they could help each other sharing ideas, especially when there were some things that the other group members knew, that they had no knowledge of. Another important source of data was the log of student email. According to Salomon as cited in [9], there are 5 stages in the development of an online learning

I. Chan Mow et al. / The Impact of CABLE on Teaching Computer Programming

61

community. The first stage is when community members develop the motivation to access and use the web environment proficiently. In the second stage, members are able to establish online identities and take the initiative to socialise with others online. Stage three is characterised by participants initiating the process of assisting and providing mutual support in information exchange. The process develops to stage four when course related group discussions eventuate and there is increased collaboration amongst members of the online community as they devise various means of collaborating in online work. Finally, the last stage, stage five, is characterised by members of the online community showing how the online learning has facilitated the achievement of personal goals and an ability to reflect on the learning process. Inspection of the log of student emails indicated that from the perceptions, processes and products of online learning using the Triple P Framework as developed by Ryba, Selby and Mentis [9], showed that the online community in Project 2, had progressed to stage three of the five stage model where students were involved in information exchange using email and the discussion forum. In terms of processes, the students were not only proficient in using the online environment, but were also using the online environment for receiving coaching, feedback and scaffolding. Students were also using the email facility for online discussion forums and for a few of them, the ability to use it for reflection on their work. In terms of products, student participation in email included: x Technical messages relating to the website or managing of the helpdesk. x Questions related to course work Lecturer participation was in the form of providing encouragement to the students, providing feedback on student queries and bringing to the notice of students valuable features of the online environment. Hence the effectiveness of the online learning environment is suggested from student interview responses, an examination of the perceptions of students, an analysis of the content of student email messages and the processes students engaged in.

4 Conclusion In essence it was found that: x CABLE, as a viable model of conceiving and delivering a high quality instructional aid system, receives a measure of strong positive support from the present results. The results showed that on the overall, students were advantaged through their participation within this program. However, at the group level, significant achievement effects for the CABLE treatment were more prominent in the case of students in the less able group as indexed on prior achievement scores. x In terms of positive attitudes towards the learning environment, results of the study indicate that, all the participants showed strong positive feelings towards their allocated treatment. x There is positive evidence for the effectiveness of online learning in all of the student interview and questionnaire responses and also from the analysis of email content and the processes students were engaged in. x The results indicate the effectiveness of CABLE as a viable instructional model for teaching Computer programming. However the results of the two studies differed in terms of which groups had benefited. In our initial study the high-ability students were clearly advantaged by CABLE. However in the current study, the low-ability students appeared to benefit slightly more than the high-ability students, although both ability groups showed clear advantages in the CABLE. Why might two similar studies yield

62

I. Chan Mow et al. / The Impact of CABLE on Teaching Computer Programming

slightly different findings? The solution we favour is that our CABLE model continues undergoing development firstly in terms of improvement and familiarity with procedures. Also in the second study the lecturing team placed relatively greater emphasis upon individualised feedback. It can be noted that the two studies differed slightly in the composition of the participant groups. In the first study, all the participants were from the Foundation program. These were generally the more able students, entering with high GPA’s. In the second study, 64% of the participants were from the Foundation program. However there were also 36% from the non-Foundation program, generally entering with low GPA’s. Despite the difference in ability levels of these two groups, there was no interaction between program and treatment, as CABLE had a positive effect on achievement levels of participants from both programs. Hence, the results of the present study have boosted the confidence of our team in the viability of CABLE. It has also confirmed that the provision of excellent instructional procedures in a richly motivating and responsive educational context can have appeal and positive effects on students who may not otherwise perform to a high level. In this respect CABLE can enable us to achieve the ultimate goal of reducing variance within student achievement levels. This is encouraging, especially within the context of teaching computer programming as the subject area is very challenging and cognitively demanding. Hence an effective instructional model would certainly improve the quality of instruction of such an inherently demanding subject and ultimately result in improved achievement levels of Computer programming students within the university.

References [1] Astrachan, O., Selby, T., & Unger, J. (1996). An object-oriented, apprenticeship approach to data structures using simulation: Proceedings of FIE '96, Frontiers in Education (pp. 130 – 134). Retrieved June 25th 2004 from [http://www.cs.duke.edu/~ola/papers/fie96.html]. [2] Garner, S. (2000). Cognitive load reduction in problem solving domains, Perth, Australia: Edith Cowan University. [3] AECT. (2001). The handbook of research for educational communications and technology. Retrieved June 12, 2003 from http://www.aect.org/intranet/publications/edtech/24/24-05.html [4] Au, W. K. (1992). Logo programming: Instructional methods and problem-solving. Unpublished doctoral dissertation. Palmerston North, New Zealand: Massey University. [5] Blair, A., & Hume, T. (1994). An exploration of the application of constructive learning techniques to software development using object orientation as a Vehicle. Paper presented at CTI Annual Conference, 1994. Retrieved March 12, 2003, from http://www.ulst.ac.uk/cticomp/therhume.html [6] Collins, A. (1989). Cognitive apprenticeship and instructional technology (Technical Report No. 474). BBN Laboratories, Cambridge, MA: Centre for the Study of Reading, University of Illinois. [7] Cooper, J., Prescott. S, Cook, L, Smith., L, Mueck, R., & Cuseo, J. (1990). Cooperative learning and college instruction: Effective use of student learning teams, California State University Foundation, Long Beach. CA. [8] Levin, J., & Waugh, M. (1998). Teaching tele-apprenticeships: Electronic network-based educational frameworks for improving teacher education. Journal of Interactive Learning Environments, 6(1-2), 39-58. [9] Ryba, K., Selby, L., & Mentis, M. (2001). Analysing the effectiveness of on-line learning found in communities. [Electronic version]. Retrieved September 25, 2003, from http:// www.ecu.edu.au/conferences/herdsa/main/papers/nonref/pdf/KenRyba.pdf.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

63

Problem Solving Process Oriented Diagnosis in Logic Programming Nguyen-Thinh Le, Wolfgang Menzel Department of Informatics, University of Hamburg, Germany {le,menzel}@informatik.uni-hamburg.de Abstract. In this paper, we present the evaluation result of our constraint-based tutoring system for logic programming from which we derive the conclusion that students need diagnostic information and remedial hints corresponding to the stage of the problem solving process where they are stuck. For this reason, we propose a three steps diagnosis approach which consists of: diagnosis at the task analysis stage, diagnosis at the solution design stage and diagnosis at the implementation stage. Our diagnosis approach should not only help students learn logic programming, but also master the skills of task analysis and solution design. Keywords: Constraint-based modeling, cognitive diagnosis, tutoring systems, logic programming, problem-solving process, instructional process.

Introduction Error diagnosis plays an important role in an Intelligent Tutoring System (ITS) because diagnostic information is essential for modelling the state of student’s knowledge and for initiating appropriate instructional actions. Currently, several programming tutoring systems apply a rather simple diagnostic approach by presenting a problem to the student and providing the possibility to submit a solution by choosing from several options or filling in a template. Diagnostic approaches supported by this type of solution submission might be used in tutoring systems aiming at helping students to become familiar with basic concepts of a programming language. In problem solving, however, students often make errors because they have difficulties with task analysis or solution design. Thus, at that point, diagnostic information about semantic or syntactic errors is not relevant. Rather it is important to consider the stage of the problem solving process, where the student becomes stuck: task analysis, solution design or implementation. We have developed a web-based tutoring system for logic programming applying the constraint-based modeling (CBM) approach. In this paper, first, we introduce the constraint-based approach briefly. In the second section, we present the result of the preliminary evaluation of our system. From the analysis of the evaluation result, we claim that diagnostic information is only useful for students if it matches the stage of the problem solving process where the student has difficulties. In the third section we outline related work. Our diagnosis approach is introduced in the fourth section. The current state and further directions of our research are summarized in the last section. 1. Constraint-based Modeling The CBM approach proposed in [1] can be applied to model general principles of a domain as a set of constraints. A constraint is represented as an ordered pair consisting of

64

N.-T. Le and W. Menzel / Problem Solving Process Oriented Diagnosis in Logic Programming

a relevance part and a satisfaction part: Constraint C =
satisfaction part>

The relevance part represents circumstances under which the constraint applies, and the satisfaction part represents a condition that needs to be fulfilled for the constraint to be satisfied. Constraints can be used to describe facts, principles or conditions which must hold for every solution contributed by the student. In addition, constraints can also be used to specify requirements of a task. Using the relevance part, constraints can be tailored according to an ideal solution, which represents the requirements of the given task. Requirements, which have to be satisfied in that specific situation, can be specified in the satisfaction part. More about the application of the CBM approach to model problem solving in logic programming can be found in [3, 4]. A constraint is evaluated by matching its relevance part to the solution. If the matching is successful then the solution should also fulfill the satisfaction part. Otherwise, the solution is considered to be incorrect with respect to the constraint that has been evaluated. If a constraint is violated, it indicates that the student solution does not obey principles of the domain or does not meet the requirements of the given task. We developed a tutoring system (INCOM) [3] for logic programming applying the CBM technique. The diagnosis approach of our current system consists of two steps. It starts by hypothesising the Prolog pattern the student solution is based on. A Prolog pattern represents a solution strategy for a programming problem [4]. For a given programming problem, there are usually several appropriate patterns which can be applied to solve it. The pattern selection is carried out heuristically. The second step of the diagnosis examines whether the student solution satisfies the task requirements. In the evaluated version, we use constraints to model task requirements. If a constraint is violated, a programming technique has been applied incorrectly or a task requirement is not fulfilled. 2. Evaluation and the Problem of Remedial Hints 2.1 Evaluation results We have conducted a preliminary evaluation for INCOM during the winter term 2004/05 at the University of Hamburg. We provided students with four exercise assignments: 1. Define a predicate which specifies the relationship between a list and its prefix. 2. Write a function to convert Peano numbers to integer numbers. 3. Write a predicate which defines an even Peano number. 4. Write a function to compute the sum of compound interest for a given amount, an interest rate and a duration in years. Students have been requested to consult our system via a web interface when experiencing difficulties in solving those four exercise assignments. On our server machine, we registered 261 log files created by 99 users. Table 1 number of false and correct trials for each task trials for a correct solution task solved

Task

trials/user

task not solved

1

6.07

4.33

11

7

2

6.21

6.54

22

23

3

5.72

6.83

27

17

4

6.21

74.5

1

24

The first goal of our evaluation was to find out which task is challenging for students. Table 1 provides the results of problem solving for each task. The 2nd and 3rd

N.-T. Le and W. Menzel / Problem Solving Process Oriented Diagnosis in Logic Programming

65

columns show, how many trials in average a user carried out, and how many trials he/she needed to reach a correct solution. The last two columns tell us, how many log files contained correct solutions and how many did not. From the last two columns we can identify that most students could solve Task 1 and 3. For Task 2, the numbers of successful and unsuccessful attempts are almost the same. We also noticed that most users could not solve task 4. Analyzing the log files, we found two main reasons for these frequent failures: 1. Task 4 is more complicated than the other tasks. It includes the concept of recursion, arithmetic expressions, and arithmetic computation; 2. Many users were not able to derive a correct formula for the computation of a compound interest. The second goal of our evaluation was to identify the problems of students and where they are usually stuck. By investigating the log files, we recognized that most errors have been detected by the first step of the diagnosis - the pattern identification process. Most users had one of the following problems: • Users were not familiar with the data structure of Peano numbers. Some of them simply input “Peano” as arguments and expected that to be a Peano number. • The arithmetic evaluation mechanism in Prolog poses considerable problems for many users. Some of them placed an arithmetic expression at an argument position and expected a functional evaluation. Others used “=” instead of “is” for arithmetic evaluation, as it is common in mathematical notations. • Users called auxiliary predicates without defining them in the hope that they are builtin predicates. Or, they used arbitrary material at an argument or subgoal position and expected that the system is able to provide helpful hints. Through errors detected during the second step of the diagnosis - the constraint evaluation process, we noticed that users had the following problems: • Many users applied arithmetic expressions without making sure that the arguments are sufficiently instantiated. Sometimes, they transposed the positions of operands and result arguments or used operands not correctly. • Instead of decomposing an input argument, many novice programmers composed it in recursive subgoals. Or, they decomposed an input argument and processed it, but then, they did not know how to return the result of the processed input value. This indicates that Prolog novices are not familiar with composition and decomposition. The third goal of our evaluation was to determine the efficacy of the system. Our system provides three levels of feedback. If the system detects an error in the user’s solution, first, it notifies that the solution is incorrect; second, upon request, it shows the problem location, gives an explanation and third, it provides suggestions to remove the error. We evaluate the efficacy of the system by determining if errors disappeared after users have seen the error location or a remedial hint. A remedial hint includes an error explanation and a correction proposal. In 75.6 % of a total of 632 false trials, students were interested in system feedback. In 60.5% of 478 feedback requests, after seeing the error location, they requested more detailed error explanation and remedial hints. That indicates that most students are interested in receiving feedback from the system in order to improve their solutions. In 68.3% of cases after seeing the error location without requesting remedial hint, users were able to remove the error. In 75.8% of cases after requesting remedial hints, the error was eliminated. As expected, the efficacy of remedial hints is higher than that of error location because remedial hints give more information. In general the system is helpful for students. However, the efficacy of our current system does not satisfy our ambitions. We investigated the log files to trace back how students have corrected their solutions after having read the feedback. We noticed that students could not correct their solutions according to some remedial hints provided by our system. This can be attributed 1) to the

66

N.-T. Le and W. Menzel / Problem Solving Process Oriented Diagnosis in Logic Programming

incoherence of the remedial hints which are specified for isolated constraints [5, 6] and 2) to a mismatch between the feedback and the stage of the problem solving process where the student’s difficulty occurred. In this paper, we mainly address the second problem. 2.2 The problem of solution remediation We illustrate the problem of providing students with appropriate feedback with the following example. The third exercise assignment requests students to define a predicate which specifies the relationship between a list and its prefix. Our system expects a correct solution like IP1 or IP2 which uses an auxiliary predicate append. Task: define a predicate to examine the relationship between a list and its prefix Solution IP1: prefix([], _). prefix([H|R], [H|T]):-prefix(R,T).

Solution IP2: IP2: prefix(L1, L2):-append(L1, Rest, L2).

A student submitted the following solution SP1 for the task above. Our system hypothesizes that the student decided to apply the strategy IP1, it then evaluates the relevant constraints and returns the corresponding diagnostic information. Student solution SP1:

prefix(List, List). prefix([], List).

Remedial hints: Error1: a base case in your solution is superfluous. Error2: a recursive case in your solution is missing.

The solution indicates that the student is in a position to specify a base case “prefix([], List).” but not able to specify a recursive case. Perhaps the student wanted to specify the type restriction for the argument positions by giving the clause “prefix(List, List)”. More likely, however, it is that the student does not know how to specify a list data structure which is required for both predicate arguments. That means he/she is not able to fully analyze the task and to specify the arguments correctly. Therefore, remedial hints concerning solution design are not helpful for the author of the solution above. In this case, we need to help the student analyze the task requirements. The task analysis includes questions like: Which information should be represented as an argument? What kind of data structures should be specified for predicate arguments? Which mode should an argument have? The following student’s solution SP2 indicates that he/she has succeeded with the task analysis, but is now struggling with designing a solution for the given task. Student solution SP2:

prefix([X], [X]). prefix(L, [X|Rest]):- append(H, Rest, [X|Rest]),prefix(H, Rest).

Remedial hints: Error1: the subgoal prefix(H, Rest) is superfluous. Error2: the clause prefix([X], [X]) is superfluous. Error3: L should be unified with H. Error4: the argument [X|Rest] in the head of 2nd clause should be represented as a variable.

The system hypothesizes that the student was following strategy IP2 and evaluates the corresponding constraints. While the first two feedback messages concern the solution design corresponding to the strategy IP2, the last two consider the erroneous implementation of arguments. This might have caused the student to be confused because she/he is currently having problems with designing the solution, not with the

N.-T. Le and W. Menzel / Problem Solving Process Oriented Diagnosis in Logic Programming

67

implementation. At this stage, the student has to deal with the questions: what kind of clauses and subgoals are required to construct a solution according to the intended design strategy, and how these clauses and subgoals have to be arranged? Table 2 tells us the proportion of false attempts due to errors in task analysis and in solution design. The second and the third columns indicate the absolute number of students’ attempts to solve the tasks. We notice that students made most errors (70%) at the stage of analyzing Task 3. That means, they were not able to specify a Peano number correctly. We also see that students had difficulties with designing solutions for Task 4. 42% of their attempts for Task 4 were not successful at finding appropriate clauses or subgoals. Table 2 Proportion of false attempts due to errors in task analysis and in solution design. Task Total attempts False attempts Errors in task analysis Errors in solution design 1

91

70

7%

20%

2

242

205

25%

18%

3

246

210

70%

17%

4

149

147

9%

42%

From the evaluation result and the analysis of student’s programs above, we can derive the need for a diagnosis approach which is able to provide diagnostic information corresponding to the stage of the problem solving process. 3. Related works Available tutoring systems are able to detect semantic or syntactic errors in a program. However, this kind of diagnostic information is not useful for students who already have difficulties in the early phases of problem solving. The problem is to determine which level of understanding the student has and how to guide him/her to correct his/her solution in a way he/she is supposed to do. Various attempts have been developed in this direction, but none of them is really able to provide diagnostic information tailored to the stage where the difficulties occurred. The Pascal tutoring system [7] is able to infer the student’s intention and to diagnose errors by mapping a student program to programming plans. This system focuses the diagnosis mainly on the solution design applying programming plans and misses the diagnosis at the task analysis stage. A model-tracing tutor [8, 9] follows the student’s intention by forcing the student to act as an expert would do. Hence, a model-tracing tutor always pretends to know the student’s intention. However, model tracing does not guarantee that student errors can always be corrected. When a student performs an act, which is neither on a correct path nor on a anticipated incorrect one, model tracing has nothing to say other than that is probably incorrect [10]. ELM-PE provides a syntax-based structure editor, which guides the student filling in appropriate insertions into predefined LISP statement slots, such that only valid LISP expressions may be constructed [11]. The diagnostic approaches mentioned restrict students’ creativity and do not support them to improve the problem solving skill. Some other approaches introduce different abstraction levels of errors made by students. The approach in [12] represents student’s actions and errors in terms of knowledge applied in a learning context. Two levels of knowledge are differentiated. The micro-level contains elements describing problems, operators, and control structures and the macro-level describes conceptions. The micro-level represents the way a conception may be revealed by a student, whereas the macro-level represents conceptions in terms of knowledge. The diagnosis approach is driven by taking into account student’s actions related to a particular task and the system provides explanations on the student’s reasoning

68

N.-T. Le and W. Menzel / Problem Solving Process Oriented Diagnosis in Logic Programming

by recognizing sub-jacent knowledge. According to [12], an environment that intends to provide personalized feedback must be able to interpret student’s actions in terms of knowledge. The approach in [13] distinguishes the surface level student model from a deeper level student model. The former one represents the scheduled problem solving plans and applied procedural knowledge. The authors of [13] argue that just diagnosing problem solving knowledge applied by the student is not sufficient, because the sequence of the procedures the student has used may reflect his or her belief in the domain axioms. Therefore, it is necessary to build a deeper level student model which consists of diagnostic hypotheses explaining the procedural operations of a student in terms of the domain axioms. Both approaches [12, 13] introduce different levels of knowledge which can be inferred from the student’s input. However, they do not provide diagnostic information along the process of problem solving. We propose a diagnostic approach which not only enables students to input a solution for a given task in free form. It also supports the students at all three stages of the problem solving process1: task analysis, solution design and implementation. 4. Three Steps Diagnosis in Logic Programming 4.1 Diagnosis at the task analysis stage To create a logic program, first, it is necessary to know how many arguments are required to solve the given task. Normally the number of required arguments can be inferred from the task specification. The student should be able to understand the functionality of every argument which is used to define a predicate. If an argument does not have any function, it is considered to be superfluous. If information from the given task has not been modeled as an argument in the predicate definition, then the student has missed a necessary argument to solve the given task. The second step is to determine the argument modes. In logic programming, an argument can have input mode, output mode or both. Students are requested to specify a mode for each argument of the predicate to be defined according to the given task. The last step of task analysis is to define appropriate data structures for the argument positions. A data structure for an argument in logic programming can be an atom, a number, a list or a special kind of term (i.e. Peano number). We request students to input the information for predicate declaration, i.e. argument list, argument mode and data structure, before they submit a complete solution. The system examines their inputs of the task analysis as the following example shows: Task description: please define a predicate which specifies the relationship between a list and its prefix. You can use the built-in predicate append if necessary. System: please, input the predicate name and the list of arguments with the appropriate modes. Student: prefix(?List1, -List2) System: Error location: 2nd argument; Explanation: wrong mode; Suggestion: check the task specification and choose the appropriate mode. Student: prefix(?List1, ?List2) System: The declaration for the predicate is correct.

1

Our term “problem solving process” differs from the definition in [13] where a sequence of operations applied by the student in solving a given problem is called a problem solving process.

N.-T. Le and W. Menzel / Problem Solving Process Oriented Diagnosis in Logic Programming

69

4.2 Diagnosis at the solution design stage Now, the student has analyzed the task correctly. The system allows the student to put in a solution. A template prefix(List1, List2) according to the predicate declaration is given to the student. After the student has submitted her/his solution, the system hypothesizes the design strategy of the student [4] and returns appropriate feedback. System: please, input your solution for the task above. Student: prefix([X],[X]).

prefix(L,[X|Rest]):-append(H,Rest,[X|Rest]),prefix(H,Rest).

System: we hypothesize that you apply the built-in predicate append to concatenate the first list with an arbitrary second list to get the third list. Please, consider the following feedback: Error1: Location: clause prefix([X],[X]) Explanation: this clause is superfluous. It is not necessary for this design strategy. Remedial hint: please remove it. Error2: Location: prefix(H,Rest) Explanation: this subgoal is superfluous. It is not required for this design strategy. Suggestion: please remove it. Student: prefix(L,[X|Rest]):-append(H,Rest,[X|Rest]).

4.3 Diagnosis at the solution implementation stage Once, the student’s solution conforms with the design strategy the system hypothesized, the system moves the diagnosis to the implementation level. This step deals with the questions: How is a necessary subgoal implemented? How are the arguments in the student’s solution implemented? Our system replies to the last student’s input in the example above as follows: System: please, consider the following feedback Error 1: Location: L, H Explanation: We need to process L. Therefore L and H have to be co-referenced. Remedial hint: write a subgoal L=H, or replace H by L, or replace L by H. Error 2:Location: [X|Rest] in the head of clause 1 Explanation: This argument position needs a variable which represents a list. Suggestion: choose a variable name. Error 3:Location: [X|Rest] in subgoal 1, clause 1 Explanation: This argument needs to represent the concatenation of two lists. Suggestion: choose a variable name instead using a list representation

Figure 1: The three steps of the diagnosis process

70

N.-T. Le and W. Menzel / Problem Solving Process Oriented Diagnosis in Logic Programming

A subgoal can be: unification, de/composition, calculation, an arithmetic test, binding, relation, recursion or user defined. An argument can be a variable, anonymous variable, list, atom (letter), number (float, int), Peano number, arithmetic expression or arbitrary term. For each subgoal, there are different implementation techniques. For example, to implement an arithmetic test subgoal “less then”, we have three possibilities: Y=Y or Y>X. The whole diagnosis process is illustrated by Figure 1. 5. Conclusion and Future Work We have presented the evaluation of our current tutoring system for logic programming. The diagnosis component of this system is developed applying the constraint-based modeling approach. The evaluation result indicated that students have not only difficulties on the on the implementation level, but also on the task analysis level (e.g. data structure for Peano numbers) and the solution design level (e.g. de/composition of lists). The evaluation result has shown that it is necessary to devise a diagnosis approach which is able to deliver diagnostic information corresponding to the stage of the problem solving process where the student is stuck. For this purpose, we have proposed a three step diagnosis approach: 1) diagnosis at the problem analysis stage, 2) diagnosis at the solution design stage and 3) diagnosis at the implementation stage. The three step diagnosis approach can serve several educational purposes. First, it helps students master analysis skills. Second, it supports students in solving programming problems by using design strategies and lastly, students become familiar with the semantics of logic programming. The diagnostic component of our system is under restructuring. We plan to launch and to evaluate a second version of our tutoring system during the winter term 2006/2007. References [1] Ohlsson, S. (1994). Constraint-based Student Modeling. In J. E. Greer, G.I. McCalla, Student Modelling: The Key to Indivi-dualized Knowledge-based Instruction, 167-189. Berlin. [2] Suraweera, P., Mitrovic, A., and Martin, B. (2005) A Knowledge Acquisition System for Constraintbased Intelligent Tutoring Systems. http://www.cosc.canterbury.ac.nz/tanja.mitrovic/SuraweeraAIED05.pdf [3] Le, N.T., and Menzel, W. (2005) Constraint-based Error Diagnosis in Logic Programming. In Proceedings of the 13th International Conference on Computers in Education. [4] Le, N.T. (2006) Using Prolog Design Patterns to Support Constraint-Based Error Diagnosis in Logic Programming. ITS workshop on Intelligent Tutoring Systems for Ill-Defined Domains. [5] Kodaganallur, V., Weitz, R.R., and Rosenthal, D. (2005) A Comparison of Model-Tracing and Constraintbased Intelligent Tutoring Paradigms. In International Journal of Artificial Intelligence in Education, Vol. 15, 117-144. [6] Menzel, W. (2006) Constraint-based Modeling and Ambiguity. In International Journal of Artificial Intelligence in Education, Vol. 16, Nr. 1. [7] Johnson, W. (1986) Intention-based Diagnosis of Novice Programming Errors. Morgan Kaufmann. [8] Anderson, J.R., and Reiser, B. (1985) The LISP Tutor. Byte, 10, 159-175. [9] Anderson, J.R., Conrad, F.G., and Corbett, A.T. (1989) Skill Acquisition and the LISP Tutor. Cognitive Science 13, 467-505. [10] Martin, B. (2001) Intelligent Tutoring Systems: The Practical Implementation of Constraint-based Modelling. PhD thesis, University of Canterbury. [11] Weber, G., and Möllenberg, A. (1995) ELM Programming Environment: a Tutoring System for LISP Beginners. In Cognition and Computer programming, 373-408. [12] Webber, C. (2004) From Errors to Conceptions – an Approach to Student Diagnosis. In Proceedings of the 7th International Conference on Intelligent Tutoring Systems. [13] Matsuda, N. and Okamoto, T. (1992) Student Model Diagnosis for Adaptive Instruction in ITS. In Proceedings of the 2nd International Conference on Intelligent Tutoring Systems., 467-474.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

71

Programming Teaching Support System Using Student Model KeunWoo Han, EunKyoung Lee, YoungJun Lee, Korea National University of Education, Korea [email protected] Abstract: This paper describes the CTi system that supports teachers in C programming courses. The CTi system provides teachers with student’s model, various learning information and inductive instruction tools. Teachers using the system can provide effective feedback to students. Bayesian Belief Networks are used to represent a student’s model and relationship between the structure of programming language and the student’s knowledge. An empirical study showed the CTi system had positive effects on students’ programming performance. Keywords: Programming education, student modeling, teaching support tool, inductive instructional method

1. Introduction Computer programming is a difficult skill for many students. New methods and techniques to help novices to learn programming are needed. Programming is difficult because of the abstract concepts. Students have problems in different issues related to program construction [6]. Therefore, it is necessary to support teaching and learning with various learning strategies such as project based learning [5], self-regulated learning [12], pair programming [2]. There were, however, few systems that support teachers to teach with various learning strategies. We developed a C programming teaching support system. It helps teachers to understand their students’ programming ability and support their teaching activities. We have used Bayesian Belief Networks to represent a student’s model and proposed a teaching support system using the inductive instructional method. Many teachers taught their class using the deductive instructional method. However, a programming language is similar to a secondary foreign language. It is better to provide students with various examples than to provide few examples. With various examples, students are not only able to experience with more the programming codes but also learn some new concepts. A good practice in computer programming teaching is to teach general concepts first and then progressively teaches more detailed grammars [9]. An inductive teaching is better for long-term retention and transference of concepts. The developed CTi system supports the inductive instructional strategy and provides various tools to support teachers. 2. The CTi System The CTi system is a C programming Teaching support system using the inductive instructional method. Students or teachers can connect to the system via a web browser. They can access many practice examples, tools and the student model on the web system. The system records all learning activities and is able to infer students’ knowledge about the

72

K. Han et al. / Programming Teaching Support System Using Student Model

concepts of the programming language. 2.1 Teaching Support tools We need to consider the inductive learning strategy in programming teaching environment [7]. It is necessary to provide students with good example codes and references such as manual. It is, also, necessary to provide teachers with theirs students’ models. It helps them to give effective feedback to their students. In the following, we illustrate the tools that are used to support teaching in the inductive instruction environment. x Annotation: Annotation can provide learning information such as learning covered and assessment. The annotations are useful for effective navigation support [11]. A screenshot of the CTi system interface is shown Figure 1. Each subjects’ icons are presented the covered material in the class and the result of assessment. x Good examples: Good example codes are needed to learn a programming language [9]. Especially in the inductive learning environment, it is important to spend time in teaching students about how to program and code. The CTi system provides students with good examples to learn programming concepts (see in Figure 1). x Visualization: A teacher can present various codes in a programming course and can control the font size of an example code on the system. The CTi system offers the highlight function that enables teachers to emphasize some codes using colored characters. The various teaching support tools are shown in Figure 2. To show example codes and change the font size of the code, teachers can use Tool 1. x Dictionary: A dictionary tool can help students to organize their knowledge about the programming language. Students’ knowledge organization directly affects their programming learning [13]. Teachers or Students are able to search a grammar, keywords and other codes (see Tool 2 in Figure 2). The result of the search is shown in Figure 2 (see Window 1 in Figure 2). Tool 1 Tool 2 Annotation Window 1

Figure 1. The CTi interface

Figure 2. The support tools in the CTi

2.2 Bayesian Student Modeling In a programming course, students learn programming by a topic. Each topic requires some prerequisite concepts. This situation can be represented as a cause-effect relation [10]. This relationship shows prerequisite concepts and learning order. Students can gradually learn contents of each topic in the inductive instructional environment. We developed a dynamic Bayesian Network student model based on a Corbett and Anderson’ probability model [4] and Reye’s paper [10]. Each topic is represented as a node and each node has a probability value of a student’s knowledge about the topic.

K. Han et al. / Programming Teaching Support System Using Student Model

73

We represented the relationship between a structure of programming language and students’ knowledge. Since the probability of a node affects the probability of other nodes, we used Dynamic Belief Networks. We implemented Bayesian Student Models for C programming course (BSMC), in which a student model is represented a Direct Acyclic Graph (DAG). As shown in Figure 3 and Figure 4, students can access their learning activities and models on the knowledge about the programming language. An internal reflective thinking is required in a programming course [1]. The student models provide students with opportunity to have internal reflective thinking.

Figure 3. BSMC DAG ScreenG

Figure 4. The Learning Information

3. The Study 3.1 Methodology To evaluate the effectiveness of the CTi system, we had done a study with 39 university students. The study was done in an introductory C programming course. The students are taught about condition statements using the inductive method. The study participants were divided into an experimental and a control group. In the experimental group, 19 students were taught a programming learning with the CTi system. In the control group, 20 students were taught without the CTi system. Before the study, students were given a pre-test. After two hour class, they took a post-test. Students in experimental group were asked to complete a short questionnaire about the CTi system. During the study, all students’ interaction with the system was logged.

3.2 Results An independent sample t-test was conducted to compare the pre-test scores of the two groups. The difference between the two scores was not statistically significant (t=-1.011, p=.319). This indicates that there were no significant differences between the two groups. To compare the learning gain scores of the two groups, an independent sample t-test was conducted. The t-test result revealed significant differences between the learning gain scores of an experimental group and that of a control group. As shown in Table 1, the experimental group had a statistically higher learning gain scores than the control group (p<.05). Students who used the CTi system perform better in programming than students who didn’t use the system.

74

K. Han et al. / Programming Teaching Support System Using Student Model

{ˆ‰“ŒGXUGz›ˆ›š›ŠšG–•G“Œˆ™••ŽGŽˆ•šGO—–š›T›Œš›GTG—™ŒT›Œš›PG

Experimental group Control group

Mean .9842 .6700

Std. Dev. .55001 .37431

t

Sig.

2.095

.043

We examined the correlation between the learning gains and the BSMC visit count that records how many times students has viewed their knowledge about the programming language. We found a marginally significant correlation (Pearson r=.444, p=.057). This means that students who viewed BSMC achieve more learning gain. That is, BSMC can help students to learn programming language. The post-study questionnaire showed that students thought the system was useful in the programming course. 4. Conclusion and Future Work We implemented the CTi system that supports teachers in programming courses. The system represents the student model related to a programming language. The learning strategy of our system is the inductive instructional method. It is to provide students with many good examples, assessments and supporting tools. We found that the system helps students to improve their programming performance. In the future, we will study on student modeling using various learning activities and other learning strategies.

References [1] Angela Carbone, John Hurst, Ian Mitchell and Dick Gunstone (2001) Characteristics of Programming Exercises that lead to Poor Learning Tendencies:Part II. Innovation and Technology in Computer Science Education. [2] Charlie McDowell and Linda Werner (2003) The Impact of Pair Programming on Student Performance, Perception and Persistence. Innovation and Technology in Computer Science Education. [3] Conati C., Gertner A. and VanLehn K. (2002) Using Bayesian Networks to Manage Uncertainty in Student Modeling. Journal of User Modeling and User-Adapted Interaction, 12, 4, 371-417. [4] Corbett, A.T., and Anderson, J.R. (1992) Student modeling and mastery learning in a computer-based programming tutor. Intelligent Tutoring Systems, 413-420. [5] David Davenport(2000) Experience Using a Project-Based Approach in an Introductory Programming Course, IEEE TRANSACTIONS ON EDUCATION, 43, 4, 443-448. [6] Essi Lahtinen, Kirsti Ala-Mutka, and Hannu-Matti Jarvinen (2005) A Study of the Difficulties of Novice Programmers. Innovation and Technology in Computer Science Education. [7] Michael J. Prince and Richard M. Felder(2006) Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases. Journal of Engineering Education, 1-16. [8] Nicolas Guibert, Partrick Girard (2003) Teaching and Learning Programming with a Programming by Example System. International Symposium on End User Development. [9] Nunan, David (1991) Language Teaching Methodology. Prentice Hall. [10] Reye, J. (1996) A belief net backbone for student modelling. Intelligent Tutoring Systems, Proceeding of the Third International Conference, Intelligent Tutoring Systems, 596-604. [11] Rosta Farzan and Peter Brusilovsky (2005) Social Navigation Support through Annotation-Based Group Modeling. User Modeling, 463-472. [12] Susan Bergin, Ronan Reilly, and Desmond Traynor (2005) Examining the Role of Self-Regulated Learning on Introductory Programming Performance. International Computing Education Research. [13] Susan Wiedenbeck (2005) Factors Affecting the Success of Non-Majors in Learning to Program. International Computing Education Research.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

75

A Method for Creating Teaching Materials of Practical Object-Oriented Methods Education Izuru Kumea, Naoya Nittab, and Yasuhiro Takemura c Graduate School of Information Science, Nara Institute of Science and Technology, Japan b Faculty of Science and Engineering, Konan University, Japan c Faculty of Arts, Osaka University of Arts, Japan [email protected]

a

Abstract: We propose a teaching method that yields similar results to actual development projects instead of coordinating actual projects which require much labor and expense. Moreover, we propose a method of efficiently editing teaching materials from a real scale open source software product. According to this method, students can have experiences to make decisions with foresight in a realistic development process, and teachers can create teaching materials efficiently and make repeated use of them. Our method is suited to students who have acquired an understanding of object modeling and object-oriented programming but who have no experience of software development on a realistic scale. Keywords: Real Scale Teaching Object-Oriented Software Design

Materials,

Software

Engineering

Education,

Introduction Professional software developers are required to make decisions with foresight on software design, that is, a skill to foresee future changes and to apply the object-oriented principles and techniques they learned to their design. Experiences of developing a real scale software system are effective to learn such a skill. However, a real project must succeed, which will make it unacceptable to adopt an educational attitude towards tolerating mistakes. In addition, to introduce a real scale software project in higher education may require too much cost and labor compared to its educational benefits. In this paper we propose a practical training method which uses a real scale teaching material so that the training process can simulate various practical software changes in a real scale software development project. Our practical training enables inexperienced students to witness the serious results of their poor design, which are not tolerated in a real project, to makes students realize the importance and the benefits of decisions with foresight in a real scale software development project, and cultivate their ability to make such decisions. We also propose an approach to help teachers edit a teaching material. Our idea is to use a tagged open source software product [6] so that teachers can easily find real examples of decision with foresight and extract them to use as exercises. The method is aimed at students who have acquired the basic concepts of object modeling and techniques of object-oriented programming but who do not have experience developing real software.

1. Scheme of Practical Training A training process of our practical training consists oftraining cycles each of which

76

I. Kume et al. / A Method for Creating Teaching Materials

simulates a development phase of a real project. At the end of a training cycle teachers bring about software changes. Students can witness the robustness or vulnerability of their design and implementation against such changes, and can evaluate their decision. We aim that students can acquire the ability to make decisions with foresight and to apply proper object-oriented techniques through training cycles. A cycle consists of three steps, (1) briefing, (2) trial, and (3) evaluation. In the following we show an example cycle which simulates a development phase of an interactive diagram editor. The example cycle starts with a briefing about "the current progress" of the project to develop the diagram editing software. The system has several editing modes each of which translates user events. It can remember users' selection and can redraw presented diagrams according to users' operation. The design and implementation are assumed to be finished except for an editing mode. Figure 1 shows primary classes whose developments have already finished. A class LayerManager contains the information of all figures presented in diagrams. A class ModeSelect is designed to implement an editing mode to process mouse operation to select one or more figure.If the current editing mode is the selection mode, then a class Editor accepts a mouse event and the event is passed to ModeSelect. ModeSelect defines event handling methods which perform examination of a figure under the mouse, selecting of the figures specified by the mouse, and redrawing of the diagram to present selected figures. In this example cycle, the design and implementation of ModeSelect are assumed incomplete. More specifically, the redrawing process of ModeSelect is hidden for the students. Students are suggested that the drawing specification and design will possibly be changed in future. At the trial step, the students are given a task to complete the design and implementation of ModeSelect. The redrawing process of ModeSelect requires the functions provided by LayerManager. Careful students can notice a possibility that the design around LayerManger will change in future, which is suggested at the briefing step. The trial step finishes when the students complete the design and implementation of ModeSelect, and the class passed tests. At the start of the evaluation step, the teacher of the practical training performs several patterns of changes. A change impact around LayerManager reaches ModeSelect if it has a careless design. The teacher reveals the hidden parts of the implementation of ModeSelect as an example of robust design, and explains its designer's decision with foresight, the object-oriented technique and the way of thinking behind of the technique, the law of Demeter [2], for example. The students evaluate not only their design but also their design decision at the trial step. The witness of the change influence and the evaluation motivate the students to learn object-oriented design techniques and way of thinking.

Figure 1. Architecture of Event Handling Framework

I. Kume et al. / A Method for Creating Teaching Materials

77

2. Expected Educational Effects Software changes at evaluation steps in our practical training show students their design vulnerability. Students can experience design mistakes which are important from the viewpoint of software engineering education but are not permissible in a real software development project. Experiences make students consider change impacts from software components developed by others and understand the importance of communication in a development team. Students try to find object-oriented design techniques applicable to realistic problems instead of being given such techniques with examples. It helps students’ understanding from a practical viewpoint. Introducing reality into training cycles is important from the educational viewpoint. Real scale software project has frequent changes. Coping with changes and influence of design mistakes are harder in larger software projects. To experience realistic problem and difficulty requires not only designs and programs by students but also a large amount of artifacts developed by "other members in the development team". It is desirable to use real scale teaching materials that include a large amount of program code and artifacts which assumed to be developed by "other members".

3. Patterns for Materials Editing This section proposes an efficient method for editing teaching materials from an existing real scale software product, which we call a raw material. Teachers must construct the practical training scenario in such a way that a training cycle induces issues on a later change, as described in the section entitled “Scheme of Practical Training”. In order to form these inductions it is therefore necessary to extract related artifacts from the raw materials. It is further necessary to express the decisions taken during the creation of these artifacts in object-oriented terminology so that the teacher can explain them in the evaluation step. In addition, the teachers must identify the parts within raw materials which reflect the subject they wish to teach. For example, in editing the teaching materials presented in the section entitled “Scheme of Practical Training” the areas where implementation was simplified by the introduction of Demeter’s law (i.e., ModeSelect) had to be specified within the interactive diagram editor. It is often the case that these types of information are not explicitly expressed in the given software. Therefore, in order to make teaching materials suited to the objectives of practical training, teachers are required to explore large artifacts and huge amounts of source code. To reduce this burden on the teachers we propose a method of placing tags at various points in the raw materials, explicitly expressing information related to the implicit influence of decisions within the raw materials. A tag placed in a certain artifact X can represent information such as the object-oriented principles and methods used to construct X, and other artifacts influenced by the construction of X. By the tags, teachers can easily reverse engineer artifacts to identify instances of teaching subjects, and edit the practical training scenario following the relationships between artifacts. In this way the construction of teaching materials can be considered straightforward. The pattern of content specified in a tag consists of six pieces of information: (1) the name of a principle or technique (teaching subject), (2) the general explanation of the subject, (3) the general application of the subject, (4) a specific instance of the subject in the artifacts, (5) the reason for using the subject in the instance, (6) a specific area of another artifact that benefits from the instance of the subject. For example, to construct a training cycle presented in the section entitled “Scheme of Practical Training”, the following tag is helpful: (1) the law of Demeter, (2) "don't talk to strangers", (3) reducing the number of

78

I. Kume et al. / A Method for Creating Teaching Materials

dependency relationships among classes, (4) Editor, (5) because Editor hides LayerManager, (6) ModeSelect. Given this information, the teachers can easily discover that a task tocomplete ModeSelect would be beneficial to learn Demeter’s law and that it would be necessary to explain Editor, LayerManager, etc., in the briefing step.

4. Related Work Many researchers have observed the importance of engendering a sense of the nature of real scale software in software engineering education.(e.g. [3]) We hope to go further. A practical training should be more than an experience of successful software development. Teachers should design a training plan and prepare teaching materials in order to accomplish their educational purposes.Using a blanked source code of a real system as a teaching material, as seen in our practical training, is not new. For example, Tanennbaum and Woodhul [5] used MINIX, a real operating system as a teaching material.Our practical training differs at the point that it requires students not only to fill blanks but also to be aware of impacts of future changes. As a result it cultivates a skill to make a decision with foresight and motivate students to learn object-oriented design techniques listed in design patterns [1]. Our idea to put tags on a raw material comes from the concept of traceability that links designs to decisions behind them [4], although the research in [4] deals with a broader range of software development concepts such as stakeholders, assumptions behind a requirement specification, and alternatives discussed but not selected.

5. Conclusion A practical training method which makes effective use of real scale software was proposed. Students can witness the impact of software changes in a real scale software system, and acquire the ability to decision with foresight. Teachers can easily edit a teaching material which enables students to experience software changes and their difficulty. Hereafter, it is desired to incorporate the method into an actual curriculum in higher education and concretely investigate its benefits and problems.

Acknowledgments This study was partly supported by Grants-in-Aid for Scientific Research of Scientific Research (C) (No.17500662), from Japan Society for the Promotion of Science.

References [1] Gamma, E., et al. (1994) Design Pattern: Elements of reusable object-oriented software, Addison-Wesley. [2] Lieberherr, K., etal. (1988) Object-oriented programming: An objective sense of style, Proc. ACM OOPSLA’88, 323-334. [3] Lincke, S. J. (2005) Work in progress - Motivating students for software engineering, ASEE/IEEE Frontiers in Education Conference. [4] Ramesh, B., and Jarke, M. (2001) Toward reference models for requirements traceability, IEEE Trans. on Software Engineering, Vol. 27, No. 1, 58-93. [5] Tanenbaum, A. S., et al. (2006) Operating Systems Design and Implementation, 3/E, Prentice Hall. [6] Tigris.org, (2006) ArgoUML, http://argouml.tigris.org/ and GEF, http://gef.tigris.org/ .

Science Education at School

This page intentionally left blank

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

81

 

Science Net: Effects of an e-Learning System on Elementary School Students’ Self-Regulated Learning in Science Classes Takeshi KITAZAWA a, Masahiro NAGAI b, Hiroshi KATO c, Kanji AKAHORI d a Tokyo Institute of Technology, Tokyo Jogakkan Primary School, Japan b Tokyo Metropolitan University, Japan c National Institute of Multimedia Education, The Graduate University for Advanced Studies, Japan d Tokyo Institute of Technology, Japan [email protected] Abstract: This paper describes the evaluation of our science e-learning site ‘Science Net’ which elementary school students have used it for one year. First, the findings show that it is not only being used it for checking lesson content but also for asking questions, lesson revision, and searching for science information via a bulletin board system (BBS). Second, the users who have used it for checking lessons tend to score higher in the self-regulated learning strategies than the non users from the results of the Motivated Strategies for Learning Questionnaire (Pintrich࡮De Groot 1990), in terms of 2 of the cognitive strategies (When I study for a test, I try to put together the information from class and from the book, and so on), and 4 of the self-regulations (I work on practice exercises and answer end of chapter questions even when I don’t have to; when I’m reading I stop once in a while and go over what I have read, and so on.). Keywords: Elementary School, Science Education, e-Learning, Web, Self-Regulated Learning

Introduction Trends in International Mathematics and Science Study 2003 (TIMSS2003) of International Association for the Evaluation of Educational Achievement (IEA) indicated science ability of Japanese elementary school students has been declining since 1995. Additionally, the students tend to hardly ever study at home (PISA 2000). Therefore, Japanese educators need to improve not only students’ science ability but also their motivation and interest of science. E-learning is one way which students tend to study at home because it can be used whenever and wherever via the Internet. Therefore, we have developed and evaluated science e-learning site ‘Science Net’ for elementary school students (Kitazawa et al., 2005; Kitazawa et al., 2006). The benefit for students was that they could check lessons when they were absent, downloading work sheets and ask the teacher questions with BBS. What seems to be lacking, however is not clear, because of the uncertainty of how students have used the site over long periods. And then, the usability of ‘Science Net’ is not clear. The first purpose of this paper is to determine how the students used ‘Science Net’ over the coarse of a year, and also to analyze what contents of the site are not used easily by the users. Kougo & Nojima (2004a) suggest when students learn via e-learning, they need to

82

T. Kitazawa et al. / Science Net: Effects of an e-Learning System

apply self-regulated learning (SRL). Self-regulated learning is defined as a learner's intentional efforts to manage and direct complex learning activities (Zimmerman, 1998). The effects of self-regulated learning is more efficient for students’ grades than the elaborated epistemic learning model (Pintrich࡮De Groot, 1990; Zimmerman࡮MartinezPons, 1990). Accordingly, the second purpose will be to investigate students’ self-regulated learning strategies with the Motivated Strategies for Learning Questionnaire (Pintrich࡮De Groot, 1990).

1. Overview of ‘Science Net’ 1.1 Outline of ‘Science Net’ The e-learning site ‘Science Net’ is for elementary school students and is Web based because students can access anywhere (Kitazawa et al., 2005). The contents of the site are 1) checking lessons, 2) asking science questions via BBS (using free software ‘Kent Web’ (http://www.kent- web.com/) (see figure1)), and much more. The site was re-launched at the beginning of the 1st semester (Apr. 2005), and used by elementary school students from 3rd to 6th grade for one year (Mar. 2006). At first, Students learnt how to use ‘Science Net’ in the PC room. After that, they were given their ID. Then, they could freely access it at school or at home. If they didn’t want to use ‘Science Net’, they weren’t forced to use it.

Writing Area Submit Button Student’s Question Teacher’s Answer

Figure 1 BBS

Schedule Icon

Unit Icon of Each Grade

BBS Reference

Figure 2 Schedules of Lessons (3rd Grade)

Contents Icon

Classes

83

T. Kitazawa et al. / Science Net: Effects of an e-Learning System

Classes Documents Unite

Keywords

Digital Materials

Homework

Total of Lessons

Figure 3 Contents of Lessons (3rd Grade)

Table 1 Number of Science Net Users and Non Users Number of Students Grade 3rd 4th 5th 6th Total

Assessment of ‘Science Net’ (on Mar. 2006) 16 19 15 13 63

Investigation of Self-Regulated Learning Strategies (on Apr. 2006) 77㧔included 16 users㧕 80㧔included 19 users㧕 78㧔included 15 users㧕 235㧔included 50 users㧕

1.2 Improvement of ‘Science Net’ One of the points of improvement is adding 3rd grade’s contents so they can check their lessons (figures 2 & 3). Another point of improvement is to check previous lessons which they did 1 year ago because the previous lessons are saved as a reference for the users. In addition, the questions which were asked by prior students are also saving in the database of BBS as a reference.

2. Method 2.1 Assessment of ‘Science Net’ First, this paper aims to analyze how the students use ‘Science Net’ for a year. 63 students who have used ‘Science Net’ for a year did a questionnaire about the site at the end of the science lesson (on Mar. 2006). The questionnaire about ‘Science Net’ has 16 inquiries items, it measures from 1.strongly disagree to 5.strongly agree. 2.2. Investigation of Self-Regulated Learning Strategies Second, this paper aims to investigate the students who use ‘Science Net’ and have self-regulated learning strategies. At the beginning of the science lesson (on Apr. 2006) 50 users and 185 non users did the self-regulated learning strategies section of Motivated Strategies for Learning Questionnaire (Pintrich ࡮ De Groot 1990), it measures from 1.strongly disagree to 7.strongly agree (see table 1).

84

T. Kitazawa et al. / Science Net: Effects of an e-Learning System Table 2 Result of Questionnaire (Assessment of ‘Science Net’, 5 choices) 㪠㫋㪼㫄㫊㩷

㪘㫍㪼㫉㪸㪾㪼㩷

㪪㫋㪸㫅㪻㪸㫉㪻㩷㪛㪼㫍㫀㪸㫋㫀㫆㫅㩷

㪨䋱㩷 㪠㩷㪺㪿㪼㪺㫂㪼㪻㩷㪸㩷㫃㪼㫊㫊㫆㫅㩷㫎㫀㫋㪿㩷㵬㪪㪺㫀㪼㫅㪺㪼㩷㪥㪼㫋㵭㩷㫎㪿㪼㫅㩷㪠㩷㫎㪸㫊㩷㪸㪹㫊㪼㫅㫋㩷㪽㫉㫆㫄㩷㪸㩷㫃㪼㫊㫊㫆㫅㪅㩷

㪊㪅㪈㪊㩷 㩷

㪈㪅㪉㪎㩷 㩷

㪨䋲㩷 㪠㩷㫌㫊㪼㪻㩷㩾㪪㪺㫀㪼㫅㪺㪼㩷㪥㪼㫋㩾㩷㪽㫆㫉㩷㫇㫉㪼㫇㪸㫉㪸㫋㫀㫆㫅㩷

㪊㪅㪊㪌㩷 㩷

㪈㪅㪈㪎㩷 㩷

㪨䋳㩷 㪠㩷㫌㫊㪼㪻㩷 㵬㪪㪺㫀㪼㫅㪺㪼㩷㪥㪼㫋㵭㪽㫆㫉㩷㫉㪼㫍㫀㪼㫎㪅㩷

㪊㪅㪋㪇㩷 㩷

㪈㪅㪈㪏㩷 㩷

㪨䋴㩷 㪠㩷㫌㫊㪼㪻㩷 㵬㪪㪺㫀㪼㫅㪺㪼㩷㪥㪼㫋㵭㩷 㪽㫆㫉㩷㫊㫋㫌㪻㫐㫀㫅㪾㩷㪸㫅㫆㫋㪿㪼㫉㩷㪾㫉㪸㪻㪼㫊㵭㩷 㪺㫆㫅㫋㪼㫅㫋㫊㪅㩷

㪉㪅㪍㪏㩷 㩷

㪈㪅㪉㪉㩷 㩷

㪨䋵㩷 㪠㩷㫃㫀㫂㪼㩷㫊㪺㫀㪼㫅㪺㪼㪅㩷

㪋㪅㪉㪊㩷 㩷

㪈㪅㪇㪈㩷 㩷

㪨䋶㩷 㪠㩷㪻㫆㫅㩾㫋㩷㫋㪿㫀㫅㫂㩷㪠㩷㫎㫀㫃㫃㩷㪹㪼㩷㫀㫅㫋㪼㫉㪼㫊㫋㪼㪻㩷㫀㫅㩷㫊㪺㫀㪼㫅㪺㪼㩷㫋㪿㪸㫅㫂㫊㩷㫋㫆㩷㵭㪪㪺㫀㪼㫅㪺㪼㩷㪥㪼㫋㵭㵭㪅㩷

㪈㪅㪏㪍㩷 㩷

㪇㪅㪐㪍㩷 㩷

㪨䋷㩷 㪠㩷㫌㫊㪼㪻㩷㩾㪪㪺㫀㪼㫅㪺㪼㩷㪥㪼㫋㩾㩷㫋㪿㪼㩷㫊㪸㫄㪼㩷㪻㪸㫐㩷㪼㫍㪼㫉㫐㩷㫎㪼㪼㫂㪅㩷

㪈㪅㪎㪈㩷 㩷

㪈㪅㪇㪌㩷 㩷

㪨䋸㩷 㪠㩷㪻㫀㪻㫅㵭㫋㩷㫌㫊㪼㩷㩾㪪㪺㫀㪼㫅㪺㪼㩷㪥㪼㫋㩾㩷㫋㪿㪼㩷㫊㪸㫄㪼㩷㫋㫀㫄㪼㩷㪼㫍㪼㫉㫐㪅㩷

㪊㪅㪋㪌㩷 㩷

㪈㪅㪌㪈㩷 㩷

㪨䋹㩷 㪠㩷㫌㫊㪼㪻㩷㩾㪪㪺㫀㪼㫅㪺㪼㩷㪥㪼㫋㩾㩷㫋㫆㩷㪺㪿㪼㪺㫂㩷㪽㫆㫉㩷㫆㫋㪿㪼㫉㫊㩾㩷㫈㫌㪼㫊㫋㫀㫆㫅㫊㩷㫎㫀㫋㪿㩷㪙㪙㪪㪅㩷

㪊㪅㪌㪉㩷 㩷

㪈㪅㪊㪇㩷 㩷

㪨㪈㪇㩷㪠㩷㫌㫊㪼㪻㩷㩾㪪㪺㫀㪼㫅㪺㪼㩷㪥㪼㫋㩾㩷㫋㫆㩷㪸㫊㫂㩷㫈㫌㪼㫊㫋㫀㫆㫅㫊㩷㫎㫀㫋㪿㩷㪙㪙㪪㪅㩷

㪊㪅㪋㪍㩷 㩷

㪈㪅㪌㪇㩷 㩷

㪨㪈㪈㩷㪠㫋㩷㫎㪸㫊㩷㪻㫀㪽㪽㫀㪺㫌㫃㫋㩷㪽㫆㫉㩷㫄㪼㩷㫋㫆㩷㫉㪼㫋㫉㫀㪼㫍㪼㩷㫈㫌㪼㫊㫋㫀㫆㫅㫊㩷㫎㪿㫀㪺㪿㩷㪠㩷㫎㪸㫅㫋㪅㩷

㪉㪅㪐㪌㩷 㩷

㪈㪅㪊㪊㩷 㩷

㪨㪈㪉㩷㪮㪿㪼㫅㩷㪠㩷㪸㫊㫂㪼㪻㩷㪸㩷㫈㫌㪼㫊㫋㫀㫆㫅㪃㩷㫋㪿㪼㩷㫈㫌㪼㫊㫋㫀㫆㫅㩷㫎㪸㫊㩷㫋㪿㪼㩷㫊㪸㫄㪼㩷㪸㫊㩷㫊㫆㫄㪼㫆㫅㪼㩷㪼㫃㫊㪼㵭㫊㩷㫈㫌㪼㫊㫋㫀㫆㫅㪅

㪉㪅㪇㪇㩷 㩷

㪈㪅㪊㪊㩷 㩷

㪨㪈㪊㩷㪠㩷㪺㪿㪼㪺㫂㪼㪻㩷㫊㫆㫄㪼㩷㫈㫌㪼㫊㫋㫀㫆㫅㫊㩷㪹㪼㪽㫆㫉㪼㩷㪠㩷㪸㫊㫂㪼㪻㩷㪸㩷㫈㫌㪼㫊㫋㫀㫆㫅㪅㩷

㪉㪅㪐㪎㩷 㩷

㪈㪅㪋㪊㩷 㩷

㪨㪈㪋㩷㪠㫋㩷㫎㪸㫊㩷㪻㫀㪽㪽㫀㪺㫌㫃㫋㩷㪽㫆㫉㩷㫄㪼㩷㫋㫆㩷㫉㪼㫋㫉㫀㪼㫍㪼㩷㪸㩷㫈㫌㪼㫊㫋㫀㫆㫅㩷㫎㪿㫀㪺㪿㩷㪠㩷㪸㫃㫊㫆㩷㫎㪸㫅㫋㪼㪻㩷㫋㫆㩷㪸㫊㫂㪅㩷

㪊㪅㪊㪍㩷 㩷

㪈㪅㪊㪋㩷 㩷

㪨㪈㪌㩷㪠㩷㫊㫋㫌㪻㫀㪼㪻㩷㫊㪺㫀㪼㫅㪺㪼㩷㫄㫆㫉㪼㩷㫋㪿㪸㫅㫂㫊㩷㫋㫆㩷 㵬㪪㪺㫀㪼㫅㪺㪼㩷㪥㪼㫋㵭㪅㩷

㪊㪅㪋㪈㩷 㩷

㪈㪅㪉㪎㩷 㩷

㪨㪈㪍㩷㪠㩷㪾㫆㫋㩷㪸㩷㪿㫀㪾㪿㩷㪾㫉㪸㪻㪼㩷㫇㫆㫀㫅㫋㩷㪽㫆㫉㩷㫊㪺㫀㪼㫅㪺㪼㩷㫋㪿㪸㫅㫂㫊㩷㫋㫆㩷 㵬㪪㪺㫀㪼㫅㪺㪼㩷㪥㪼㫋㵭㪅㩷

㪊㪅㪈㪇㩷 㩷

㪈㪅㪇㪎㩷 㩷

2.3. Performance of Science Students tested for a year. The tests calculated an average score per students (national average was 82 points.). The results were compared between the users and the non users.

3. Result 3.1 Assessment of ‘Science Net’ 3.1.1 Result of Average

Table 3 Result of Factor Analysis 㪠㫋㪼㫄㫊㩷

㪝㪸㪺㫋㫆㫉㩷㪈

㪝㪸㪺㫋㫆㫉㩷㪉㩷

㪝㪸㪺㫋㫆㫉㩷㪊㩷

㪺㫆㫄㫄㫆㫅㩷 㪾㫉㫆㫌㫅㪻 㪅㪌㪎㪊㩷

㪨㪈㩷

㪅㪎㪊㪈㩷

㪅㪇㪐㪋㩷

㪅㪈㪎㪉㩷

㪨㪈㪈㩷

㪄㪅㪍㪋㪉㩷

㪅㪇㪐㪋㩷

㪅㪈㪏㪋㩷

㪅㪋㪌㪌㩷

㪨㪊㩷

㪅㪌㪏㪋㩷

㪅㪈㪈㪈㩷

㪄㪅㪈㪋㪌㩷

㪅㪊㪎㪌㩷

㪨㪉㩷

㪅㪋㪎㪈㩷

㪅㪊㪊㪇㩷

㪄㪅㪇㪇㪏㩷

㪅㪊㪊㪈㩷

㪨㪈㪋㩷

㪄㪅㪋㪎㪇㩷

㪄㪅㪇㪐㪇㩷

㪅㪉㪏㪈㩷

㪅㪊㪇㪏㩷

㪨㪏㩷

㪄㪅㪋㪊㪎㩷

㪄㪅㪈㪈㪌㩷

㪄㪅㪇㪈㪉㩷

㪅㪉㪇㪋㩷

㪨㪎㩷

㪅㪊㪍㪇㩷

㪅㪈㪊㪈㩷

㪅㪇㪈㪌㩷

㪅㪈㪋㪎㩷

㪨㪌㩷

㪅㪇㪌㪏㩷

㪅㪏㪊㪍㩷

㪅㪇㪎㪋㩷

㪅㪎㪇㪏㩷

㪨㪍㩷

㪄㪅㪇㪌㪈㩷

㪄㪅㪎㪇㪎㩷

㪅㪇㪏㪇㩷

㪅㪌㪇㪐㩷

㪨㪈㪌㩷

㪅㪋㪏㪉㩷

㪅㪌㪉㪎㩷

㪄㪅㪇㪉㪌㩷

㪅㪌㪈㪈㩷

㪨㪈㪍㩷

㪅㪉㪎㪐㩷

㪅㪋㪇㪎㩷

㪅㪈㪎㪇㩷

㪅㪉㪎㪉㩷

㪅㪐㪉㪉㩷 㪅㪏㪌㪐㩷 㪨㪈㪇㩷 㪄㪅㪇㪎㪎㩷 㪄㪅㪇㪌㪍㩷 Table 2 indicates the results of the 㪨㪐㩷 㪄㪅㪇㪋㪎㩷 㪅㪈㪈㪏㩷 㪅㪎㪍㪋㩷 㪅㪍㪇㪇㩷 questionnaire from the assessment of 㪚㫌㫄㫌㫃㪸㫋㫀㫍㪼㩷 㪈㪏㪅㪉㪏㩷 㪊㪉㪅㪋㪈㩷 㪋㪌㪅㪇㪉㩷 㩷 㩷㩷 ‘Science Net’ (5 choices). First, the 㪚㫆㫅㫋㫉㫀㪹㫌㫋㫀㫅㪾㩷㪩㪸㫋㪼㩷㩿㩼㪀㩷 finding is the users tend to like science according to ‘Q5 I like science (average is 4.23).’ In addition, the site influence interest of science according to ‘Q6 I don't think I will be interested in science thanks to ‘Science Net’ (average is 1.71).’ The users used the system randomly rather than on a regular basis (see results of Q7 and Q8). ‘Q1 I checked a lesson with ‘Science Net’ when I was absent from a lesson.’, ‘Q2 I used 'Science Net' for preparation’, closely indicates the intermediate value (3). Therefore, it seems that they didn’t always use the site for preparation or review. Consequentially, we need to specifically analyze their awareness of these options.

3.1.2 Result of Factor Analysis First, we analyzed 16 items using factor analysis (principal factor analysis, varimax rotation) because we inquired about the users’ awareness of using ‘Science Net’. Second, we got 3 items out of the factor analysis because the factors loaded were less than 0.30. After that we did a factor analysis again. The number of the factor was defined by the result

T. Kitazawa et al. / Science Net: Effects of an e-Learning System

85

of screeplot. Table 3 shows the result of the factor analysis. We got 3 factors (cumulative contributing rate is 45.02%) which defined the first factor as ‘the use of checking lessons and retrieving questions’, the second factor as ‘the interest of science study’, and the third factor as ‘the use of BBS’. The finding that ‘Science Net’ has two uses one as ‘the use of checking lessons and retrieving questions’ and second ‘the use of BBS’. Therefore, results show that some users have used the site to check and retrieve questions, and others have used it for the BBS. It is clear that the interest of science correlates with the use of ‘Science Net’ according to the second factor ‘the interest of science study’. In addition, we need to analyze whether the use of ‘Science Net’ will bring an interest to science for students. ‘Science Net’ has had several improvements because of students’ comments. Results from students’ comments indicate that a retrieval system in the BBS is necessary. If we refer to table 4 this point becomes more evident. 3.2 Investigation of Self-Regulated Learning Strategies From the questionnaire results we are able to see how the students used ‘Science Net’. Result from the factor analysis showed that there are two groups of users. The first group of users is those who failed to check the lesson type. The second group of users is those who checked the BBS. We went on to further analyze individual students. From the results of Q2, Q3, Q9, and Q10, we analyzed the different types of individual users. We found that 16 students used the system only for checking lessons because they chose 4 (agree) or 5 (strongly agree) for Q2 or Q3, and for Q9 and Q10 their answers ranged from 1 (strongly disagree) to 3 (neutral). We defined students in this group as those who used Science Net ‘only for checking lessons’. Secondly, we found 19 students used Science Net for checking lessons as well as checking the BBS. We concluded this from their answers from Q2, Q3, Q9, and Q10. For all students chose 4 or 5 on the questionnaire. We defined these students as those who used Science Net for ‘checking lessons and the BBS’. Thirdly, 11 students used it only for checking the BBS because they chose 4 or 5 for Q9 or Q10, and for Q2 and Q3 their answers ranged from 1 to 3. We defined these students as those who used Science Net for ‘only checking the BBS’. Lastly, 4 students used it for other purposes because for Table 4 Students’ Comments which They Want to Improve Contents of ‘Science Net’ Grade 3rd

4th

Students’ comments I want you to tell me about the interest things of science. I want you to change the BBS so that I can check previous questions easier. I think it’s a good thing that questions are categorized according to grade. I will access everyday if there are some games about science on ‘Science Net’. I would like you to make a section in BBS for previous questions only. I would also like you to categorize the questions. For example, ‘plant’, ‘animal’, ‘celestial body’. I want to be able to check my friends’ questions easier䋮 If everyone doesn’t use ‘Science Net’ at home, then I suggest we decide on the day to use the site in class. When I asked a question, the question was asked already, so I didn’t ask my question. Also I would like to get some worksheets for tests. I want to be able to input my password easily.

5th

I want an independent research site, so I can do experiment at home. When I ask a question, it was the same as someone else’s question. Could you possibly improve that? I would like games on the site. I sometimes forgot my password, so I think everyone should be able to check their passwords easily.

6th

I couldn’t find my question because many questions were saved after my question. It’s terrible! It is difficult for me to retrieve a question that I want from the database.

86

T. Kitazawa et al. / Science Net: Effects of an e-Learning System

㪈

㪉

㪊

㪋

㪌

㪍

㪨㪈㩷㪮㪿㪼㫅㩷㪠㩷㫊㫋㫌㪻㫐㩷㪽㫆㫉㩷㪸㩷㫋㪼㫊㫋㪃㩷㪠㩷㫋㫉㫐㩷㫋㫆㩷㫇㫌㫋㩷㫋㫆㪾㪼㫋㪿㪼㫉㩷㫋㪿㪼㩷㫀㫅㪽㫆㫉㫄㪸㫋㫀㫆㫅㩷㪽㫉㫆㫄 㪺㫃㪸㫊㫊㩷㪸㫅㪻㩷㪽㫉㫆㫄㩷㫋㪿㪼㩷㪹㫆㫆㫂䋮

㪎 㪌㪅㪍㪍㩿㪈㪅㪌㪌㪀 㪋㪅㪐㪊㩿㪈㪅㪐㪈㪀 㪌㪅㪇㪉㩿㪈㪅㪌㪈㪀

㪂

㪨㪉㩷㪮㪿㪼㫅㩷㪠㩷㪻㫆㩷㪿㫆㫄㪼㫎㫆㫉㫂㪃㩷㪠㩷㫋㫉㫐㩷㫋㫆㩷㫉㪼㫄㪼㫄㪹㪼㫉㩷㫎㪿㪸㫋㩷㫋㪿㪼㩷㫋㪼㪸㪺㪿㪼㫉㩷㫊㪸㫀㪻㩷㫀㫅㩷㪺㫃㪸㫊㫊 㫊㫆㩷㪠㩷㪺㪸㫅㩷㪸㫅㫊㫎㪼㫉㩷㫋㪿㪼㩷㫈㫌㪼㫊㫋㫀㫆㫅㫊㩷㪺㫆㫉㫉㪼㪺㫋㫃㫐㪅

㪌㪅㪏㪇㩿㪇㪅㪐㪊㪀 㪌㪅㪉㪇㩿㪈㪅㪋㪉㪀 㪌㪅㪈㪏㩿㪈㪅㪈㪎㪀

㪁

㪨㪊㩷㪠㫋㩷㫀㫊㩷㪿㪸㫉㪻㩷㪽㫆㫉㩷㫄㪼㩷㫋㫆㩷㪻㪼㪺㫀㪻㪼㩷㫎㪿㪸㫋㩷㫋㪿㪼㩷㫄㪸㫀㫅㩷㫀㪻㪼㪸㫊㩷㪸㫉㪼㩷㫀㫅㩷㫎㪿㪸㫋㩷㪠㩷㫉㪼㪸㪻㪅

㪉㪅㪏㪊㩿㪈㪅㪌㪉㪀 㪊㪅㪈㪊㩿㪈㪅㪌㪈㪀 㪊㪅㪈㪏㩿㪈㪅㪍㪉㪀

㪨㪋㩷㪮㪿㪼㫅㩷㪠㩷㫊㫋㫌㪻㫐㩷㪠㩷㫇㫌㫋㩷㫀㫄㫇㫆㫉㫋㪸㫅㫋㩷㫀㪻㪼㪸㫊㩷㫀㫅㫋㫆㩷㫄㫐㩷㫆㫎㫅㩷㫎㫆㫉㪻㫊㪅

㪌㪅㪋㪋㩿㪈㪅㪍㪎㪀 㪌㪅㪉㪇㩿㪈㪅㪋㪎㪀 㪋㪅㪐㪍㩿㪈㪅㪍㪐㪀

㪨㪌㩷㪠㩷㪸㫃㫎㪸㫐㫊㩷㫋㫉㫐㩷㫋㫆㩷㫌㫅㪻㪼㫉㫊㫋㪸㫅㪻㩷㫎㪿㪸㫋㩷㫋㪿㪼㩷㫋㪼㪸㪺㪿㪼㫉㩷㫀㫊㩷㫊㪸㫐㫀㫅㪾㩷㪼㫍㪼㫅㩷㫀㪽㩷㫀㫋 㪻㫆㪼㫊㫅㩾㫋㩷㫄㪸㫂㪼㩷㫊㪼㫅㫊㪼㪅

㪌㪅㪉㪐㩿㪈㪅㪍㪍㪀 㪌㪅㪈㪊㩿㪈㪅㪐㪉㪀 㪋㪅㪏㪋㩿㪈㪅㪍㪏㪀

㪨㪍㩷㪮㪿㪼㫅㩷㪠㩷㫊㫋㫌㪻㫐㩷㪽㫆㫉㩷㪸㩷㫋㪼㫊㫋㩷㪠㩷㫋㫉㫐㩷㫋㫆㩷㫉㪼㫄㪼㫄㪹㪼㫉㩷㪸㫊㩷㫄㪸㫅㫐㩷㪽㪸㪺㫋㫊㩷㪸㫊㩷㪠㩷㪺㪸㫅㪅

㪌㪅㪍㪉㩿㪈㪅㪌㪏㪀 㪋㪅㪏㪇㩿㪉㪅㪉㪈㪀 㪌㪅㪇㪋㩿㪈㪅㪌㪈㪀

㪨㪎㩷㪮㪿㪼㫅㩷㫊㫋㫌㪻㫐㫀㫅㪾㪃㩷㪠㩷㪺㫆㫇㫐㩷㫄㫐㩷㫅㫆㫋㪼㫊㩷㫆㫍㪼㫉㩷㫋㫆㩷㪿㪼㫃㫇㩷㫄㪼㩷㫉㪼㫄㪼㫄㪹㪼㫉㩷㫄㪸㫋㪼㫉㫀㪸㫃㪅

㪌㪅㪐㪋㩿㪈㪅㪋㪊㪀 㪌㪅㪇㪊㩿㪈㪅㪐㪋㪀 㪌㪅㪈㪐㩿㪈㪅㪌㪏㪀

㪨㪏㩷㪮㪿㪼㫅㩷㪠㩷㫊㫋㫌㪻㫐㩷㪽㫆㫉㩷㪸㩷㫋㪼㫊㫋㩷㪠㩷㫇㫉㪸㪺㫋㫀㪺㪼㩷㫊㪸㫐㫀㫅㪾㩷㫋㪿㪼㩷㫀㫄㫇㫆㫉㫋㪸㫅㫋㩷㪽㪸㪺㫋㫊㩷㫆㫍㪼㫉㩷㪸㫅㪻 㫆㫍㪼㫉㩷㫋㫆㩷㫄㫐㫊㪼㫃㪽㪅

㪋㪅㪉㪊㩿㪈㪅㪐㪎㪀 㪊㪅㪌㪊㩿㪉㪅㪇㪇㪀 㪊㪅㪐㪋㩿㪈㪅㪎㪈㪀

㪬㫊㪼㫉㫊㩷㫎㪿㫆㩷㪿㪸㫍㪼㩷㫌㫊㪼㪻 㪽㫆㫉㩷㪺㪿㪼㪺㫂㫀㫅㪾㩷㫃㪼㫊㫊㫆㫅㫊 㪬㫊㪼㫉㫊㩷㫎㪿㫆㩷㪿㪸㫍㪼㫅㵭㫋㩷㫌㫊㪼㪻 㪽㫆㫉㩷㪺㪿㪼㪺㫂㫀㫅㪾㩷㫃㪼㫊㫊㫆㫅㫊 㪥㫆㫅㩷㪬㫊㪼㫉㫊

㪨㪐㩷㪠㩷㫌㫊㪼㩷㫎㪿㪸㫋㩷㪠㩷㪿㪸㫍㪼㩷㫃㪼㪸㫉㫅㪼㪻㩷㪽㫉㫆㫄㩷㫆㫃㪻㩷㪿㫆㫄㪼㫎㫆㫉㫂㩷㪸㫊㫊㫀㪾㫅㫄㪼㫅㫋㫊㩷㪸㫅㪻㩷㫋㪿㪼 㫋㪼㫏㫋㪹㫆㫆㫂㩷㫋㫆㩷㪻㫆㩷㫅㪼㫎㩷㪸㫊㫊㫀㪾㫅㫄㪼㫅㫋㫊㪅

㪌㪅㪏㪌㩿㪈㪅㪍㪍㪀 㪌㪅㪋㪇㩿㪈㪅㪍㪏㪀 㪌㪅㪈㪏㩿㪈㪅㪍㪏㪀

㪨㪈㪇㩷㪮㪿㪼㫅㩷㪠㩷㪸㫄㩷㫊㫋㫌㪻㫐㫀㫅㪾㩷㪸㩷㫋㫆㫇㫀㪺㪃㩷㪠㩷㫋㫉㫐㩷㫋㫆㩷㫄㪸㫂㪼㩷㪼㫍㪼㫉㫐㫋㪿㫀㫅㪾㩷㪽㫀㫋㩷㫋㫆㪾㪼㫋㪿㪼㫉㪅

㪋㪅㪋㪐㩿㪈㪅㪏㪇㪀 㪋㪅㪋㪎㩿㪈㪅㪌㪌㪀 㪋㪅㪈㪉㩿㪈㪅㪋㪏㪀

㪨㪈㪈㩷㪮㪿㪼㫅㩷㪠㩷㫉㪼㪸㪻㩷㫄㪸㫋㪼㫉㫀㪸㫃㩷㪽㫆㫉㩷㫋㪿㫀㫊㩷㪺㫃㪸㫊㫊㪃㩷㪠㩷㫊㪸㫐㩷㫋㪿㪼㩷㫎㫆㫉㪻㫊㩷㫆㫍㪼㫉㩷㪸㫅㪻㩷㫆㫍㪼㫉㩷㫋㫆 㫄㫐㫊㪼㫃㪽㩷㫋㫆㩷㪿㪼㫃㫇㩷㫄㪼㩷㫉㪼㫄㪼㫄㪹㪼㫉㪅

㪋㪅㪍㪍㩿㪈㪅㪍㪍㪀 㪊㪅㪍㪎㩿㪉㪅㪇㪍㪀 㪋㪅㪇㪊㩿㪈㪅㪎㪌㪀

㪨㪈㪉㩷㪠㩷㫆㫌㫋㫃㫀㫅㪼㩷㫋㪿㪼㩷㪺㪿㪸㫇㫋㪼㫉㫊㩷㫀㫅㩷㫄㫐㩷㪹㫆㫆㫂㩷㫋㫆㩷㪿㪼㫃㫇㩷㫄㪼㩷㫊㫋㫌㪻㫐㪅

㪋㪅㪍㪊㩿㪈㪅㪏㪉㪀 㪊㪅㪍㪇㩿㪉㪅㪇㪍㪀 㪋㪅㪈㪍㩿㪈㪅㪏㪌㪀

㪨㪈㪊㩷㪮㪿㪼㫅㩷㫉㪼㪸㪻㫀㫅㪾㩷㪠㩷㫋㫉㫐㩷㫋㫆㩷㪺㫆㫅㫅㪼㪺㫋㩷㫋㪿㪼㩷㫋㪿㫀㫅㪾㫊㩷㪠㩷㪸㫄㩷㫉㪼㪸㪻㫀㫅㪾㩷㪸㪹㫆㫌㫋㩷㫎㫀㫋㪿 㫎㪿㪸㫋㩷㪠㩷㪸㫃㫉㪼㪸㪻㫐㩷㫂㫅㫆㫎㪅

㪋㪅㪏㪏㩿㪈㪅㪏㪌㪀 㪋㪅㪌㪊㩿㪈㪅㪌㪌㪀 㪋㪅㪋㪍㩿㪈㪅㪋㪍㪀

㪁㫇㪓㪅㪇㪌 㪂㫇㪓㪅㪈

Figure 4 Result of Self-Regulated Learning Strategies (Cognitive Strategy Use: Numbers are average, number in parenthesis is standard deviation) 㪈

㪉

㪊

㪋

㪨㪈㪋㩷㪠㩷㪸㫊㫂㩷㫄㫐㫊㪼㫃㪽㩷㫈㫌㪼㫊㫋㫀㫆㫅㫊㩷㫋㫆㩷㫄㪸㫂㪼㩷㫊㫌㫉㪼㩷㪠㩷㫂㫅㫆㫎㩷㫋㪿㪼㩷㫄㪸㫋㪼㫉㫀㪸㫃㩷㪠 㪿㪸㫍㪼㩷㪹㪼㪼㫅㩷㫊㫋㫌㪻㫐㫀㫅㪾㪅

㪌

㪍

㪎 㪊㪅㪎㪐㩿㪈㪅㪎㪈㪀 㪊㪅㪋㪎㩿㪉㪅㪊㪊㪀 㪊㪅㪇㪏㩿㪈㪅㪍㪏㪀

㪂

㪨㪈㪌㩷㪮㪿㪼㫅㩷㫎㫆㫉㫂㩷㫀㫊㩷㪿㪸㫉㪻㩷㪠㩷㪼㫀㫋㪿㪼㫉㩷㪾㫀㫍㪼㩷㫌㫇㩷㫆㫉㩷㫊㫋㫌㪻㫐㩷㫆㫅㫃㫐㩷㫋㪿㪼㩷㪼㪸㫊㫐 㫇㪸㫉㫋㫊㪅

㪈㪅㪐㪎㩿㪈㪅㪌㪊㪀 㪉㪅㪇㪇㩿㪈㪅㪈㪏㪀 㪉㪅㪈㪎㩿㪈㪅㪊㪈㪀

㪨㪈㪍㩷㩷㪠㩷㫎㫆㫉㫂㩷㫆㫅㩷㫇㫉㪸㪺㫋㫀㪺㪼㩷㪼㫏㪼㫉㪺㫀㫊㪼㫊㩷㪸㫅㪻㩷㪸㫅㫊㫎㪼㫉㩷㪼㫅㪻㩷㫆㪽㩷㪺㪿㪸㫇㫋㪼㫉 㫈㫌㪼㫊㫋㫀㫆㫅㫊㩷㪼㫍㪼㫅㩷㫎㪿㪼㫅㩷㪠㩷㪻㫆㫅㩾㫋㩷㪿㪸㫍㪼㩷㫋㫆㪅

㪁

㪌㪅㪏㪍㩿㪈㪅㪋㪇㪀 㪌㪅㪋㪎㩿㪈㪅㪎㪎㪀 㪌㪅㪈㪊㩿㪈㪅㪌㪈㪀

㪨㪈㪎㩷㪜㫍㪼㫅㩷㫎㪿㪼㫅㩷㫊㫋㫌㪻㫐㩷㫄㪸㫋㪼㫉㫀㪸㫃㫊㩷㪸㫉㪼㩷㪻㫌㫃㫃㩷㪸㫅㪻㩷㫌㫅㫀㫅㫋㪼㫉㪼㫊㫋㫀㫅㪾㪃㩷㪠 㫂㪼㪼㫇㩷㫎㫆㫉㫂㫀㫅㪾㩷㫌㫅㫋㫀㫃㩷㪠㩷㪽㫀㫅㫀㫊㪿㪅

㪁

㪌㪅㪏㪏㩿㪈㪅㪉㪌㪀 㪌㪅㪇㪇㩿㪈㪅㪎㪊㪀 㪌㪅㪉㪊㩿㪈㪅㪋㪇㪀

㪨㪈㪏㩷㪙㪼㪽㫆㫉㪼㩷㪠㩷㪹㪼㪾㫀㫅㩷㫊㫋㫌㪻㫐㫀㫅㪾㩷㪠㩷㫋㪿㫀㫅㫂㩷㪸㪹㫆㫌㫋㩷㫋㪿㪼㩷㫋㪿㫀㫅㪾㫊㩷㪠㩷㫎㫀㫃㫃㩷㫅㪼㪼㪻 㫋㫆㩷㪻㫆㩷㫋㫆㩷㫃㪼㪸㫉㫅㪅

㪋㪅㪉㪊㩿㪉㪅㪇㪋㪀 㪋㪅㪋㪎㩿㪈㪅㪏㪌㪀 㪊㪅㪍㪎㩿㪈㪅㪌㪌㪀

㪨㪈㪐㩷㩷㪠㩷㫆㪽㫋㪼㫅㩷㪽㫀㫅㪻㩷㫋㪿㪸㫋㩷㪠㩷㪿㪸㫍㪼㩷㪹㪼㪼㫅㩷㫉㪼㪸㪻㫀㫅㪾㩷㪽㫆㫉㩷㪺㫃㪸㫊㫊㩷㪹㫌㫋㩷㪻㫆㫅㩾㫋 㫂㫅㫆㫎㩷㫎㪿㪸㫋㩷㫀㫋㩷㫀㫊㩷㪸㫃㫃㩷㪸㪹㫆㫌㫋㪅

㪈㪅㪍㪍㩿㪇㪅㪐㪎㪀 㪉㪅㪋㪇㩿㪈㪅㪌㪌㪀 㪉㪅㪈㪇㩿㪈㪅㪉㪎㪀

㪨㪉㪇㩷㪠㩷㪽㫀㫅㪻㩷㫋㪿㪸㫋㩷㫎㪿㪼㫅㩷㫋㪿㪼㩷㫋㪼㪸㪺㪿㪼㫉㩷㫀㫊㩷㫋㪸㫃㫂㫀㫅㪾㩷㪠㩷㫋㪿㫀㫅㫂㩷㫆㪽㩷㫆㫋㪿㪼㫉 㫋㪿㫀㫅㪾㫊㩷㪸㫅㪻㩷㪻㫆㫅㩾㫋㩷㫉㪼㪸㫃㫃㫐㩷㫃㫀㫊㫋㪼㫅㩷㫋㫆㩷㫎㪿㪸㫋㩷㫀㫊㩷㪹㪼㫀㫅㪾㩷㫊㪸㫀㪻㪅

㪈㪅㪌㪉㩿㪈㪅㪇㪍㪀 㪈㪅㪏㪇㩿㪈㪅㪇㪈㪀 㪈㪅㪐㪈㩿㪈㪅㪉㪉㪀

㪨㪉㪈㩷㪮㪿㪼㫅㩷㪠㩾㫄㩷㫉㪼㪸㪻㫀㫅㪾㩷㪠㩷㫊㫋㫆㫇㩷㫆㫅㪺㪼㩷㫀㫅㩷㪸㩷㫎㪿㫀㫃㪼㩷㪸㫅㪻㩷㪾㫆㩷㫆㫍㪼㫉㩷㫎㪿㪸㫋㩷㪠 㪿㪸㫍㪼㩷㫉㪼㪸㪻㪅

㪁

㪨㪉㪉㩷㪠㩷㫎㫆㫉㫂㩷㪿㪸㫉㪻㩷㫋㫆㩷㪾㪼㫋㩷㪸㩷㪾㫆㫆㪻㩷㪾㫉㪸㪻㪼㩷㪼㫍㪼㫅㩷㫎㪿㪼㫅㩷㪠㩷㪻㫆㫅㩾㫋㩷㫃㫀㫂㪼㩷㪸 㪺㫃㪸㫊㫊㪅

㪬㫊㪼㫉㫊㩷㫎㪿㫆㩷㪿㪸㫍㪼㩷㫌㫊㪼㪻 㪽㫆㫉㩷㪺㪿㪼㪺㫂㫀㫅㪾㩷㫃㪼㫊㫊㫆㫅㫊 㪬㫊㪼㫉㫊㩷㫎㪿㫆㩷㪿㪸㫍㪼㫅㵭㫋 㫌㫊㪼㪻㩷㪽㫆㫉㩷㪺㪿㪼㪺㫂㫀㫅㪾 㫃㪼㫊㫊㫆㫅㫊 㪥㫆㫅㩷㪬㫊㪼㫉㫊

㪋㪅㪏㪐㩿㪈㪅㪏㪏㪀 㪋㪅㪎㪊㩿㪈㪅㪉㪏㪀 㪋㪅㪈㪇㩿㪈㪅㪎㪍㪀

㪌㪅㪉㪐㩿㪈㪅㪌㪏㪀 㪌㪅㪊㪊㩿㪈㪅㪈㪈㪀 㪌㪅㪉㪌㩿㪈㪅㪌㪍㪀

㪁㫇㪓㪅㪇㪌 㪂㫇㪓㪅㪈

Figure 5 Result of Self-Regulated Learning Strategies (Self-Rgulation: Numbers are average, number in parenthesis is standard deviation) Table 5 Results of Performance Test 㪪㫋㫌㪻㪼㫅㫋㫊㩷 㪬㫊㪼㫉㫊㩷㫎㪿㫆㩷㪿㪸㫍㪼㩷㫌㫊㪼㪻㩷㵬㪪㪺㫀㪼㫅㪺㪼㩷㪥㪼㫋㵭㩷㫆㫅㫃㫐㩷㪽㫆㫉㩷㪺㪿㪼㪺㫂㫀㫅㪾㩷㫃㪼㫊㫊㫆㫅㫊㩷 䋨㫅㪔㪊㪌䋩㩷

㪘㫍㪼㫉㪸㪾㪼㩷

㪪㫋㪸㫅㪻㪸㫉㪻㩷㪛㪼㫍㫀㪸㫋㫀㫆㫅

㪐㪉㪅㪎㩷

㪌㪅㪎㪐㩷

㪬㫊㪼㫉㫊㩷㫎㪿㫆㩷㪿㪸㫍㪼㫅㵭㫋㩷㫌㫊㪼㪻㩷㵬㪪㪺㫀㪼㫅㪺㪼㩷㪥㪼㫋㵭㩷㫆㫅㫃㫐㩷㪽㫆㫉㩷㪺㪿㪼㪺㫂㫀㫅㪾㩷㫃㪼㫊㫊㫆㫅㫊㩷 䋨㫅㪔㪈㪌䋩㩷

㪐㪈㪅㪎㩷

㪋㪅㪊㪇㩷

㪥㫆㫅㩷㪬㫊㪼㫉㫊㩷 䋨㫅㪔㪉㪊㪌䋩㩷

㪐㪈㪅㪈㩷 㩷

㪌㪅㪏㪈㩷 㩷

㩷 㫅㪅㫊㪅

Q2, Q3, Q9, and Q10 they chose 1, 2 or 3 on the questionnaire. We defined this last group

T. Kitazawa et al. / Science Net: Effects of an e-Learning System

87

of users as those who used Science Net for ‘other purposes’. From the above results it indicated there might be a difference between the students’ purpose for using Science Net and the students’ self-regulated learning strategies. Therefore, we compared the different self-regulated learning strategies with the Motivated Strategies for Learning Questionnaire (Pintrich࡮De Groot 1990), however it is not significantly different between 4 groups by ANOVA. But it is considered that the students who have used the system for checking lessons have higher self-regulated learning strategies such as ‘rehearsing and memorizing’ or ‘reviewing records’ (Zimmerman 1989) than the students who have not used it for checking lessons because they checked the lessons constantly. Therefore, we compared the different self-regulated learning strategies between 35 students who have used it for checking lessons (the users who used it only for checking lessons and the users who used it for checking lessons and the BBS), 15 students who haven’t used it for checking lessons (the users who used it only for checking the BBS and the users who used it for other purposes), and 235 non users. Figures 4 & 5 indicate the results of the self-regulated learning strategies section of Motivated Strategies for Learning Questionnaire (7 choices). 2 cognitive strategy use items tested by ANOVA show significant difference or significant tendency as below; ‘Q1 When I study for a test, I try to put together the information from class and from the book (F(2,232)=2.61, p<.1).’, ‘Q2 When I do homework, I try to remember what the teacher said in class so I can answer the questions correctly (F(2,232)=-4.22, p<.05).’ 5 self-regulation items tested by ANOVA show significant difference or significant tendency as below; ‘Q14 I ask myself questions to make sure I know the material I have been studying (F(2,229)=2.51, p<.1).’, ‘Q16 I work on practice exercises and answer end of chapter questions even when I don't have to (F(2,231)=3.54, p<.05).’, ‘Q17 Even when study materials are dull and uninteresting, I keep working until I finish. (F(2,231)=3.54 p<.05).’, ‘Q18 Before I begin studying I think about the things I will need to do to learn (F(2,228)=2.97 p<.1).’, ‘Q21 When I'm reading I stop once in a while and go over what I have read (F(2,230)=3.50, p<.01).’ These enquiry items were analyzed multiple comparison of Tukey. The findings are ‘Q1 (p<.1)’, ‘Q2 (p<.05)’, ‘Q14 (p<.1)’, ‘Q16 (p<.05)’, ‘Q17 (p<.05)’, ‘Q21 (p<.05)’ had significant difference or significant tendency between the users who have used it for checking lessons and the non users. It had no significant different between the users who haven’t used it for checking lessons and the non users. It is not too far from the truth to say that the ‘Science Net’ users who have used it for checking lessons tend to try to make everything fit together more than the non users from these results. It is clear that when the users who have used it for checking lessons study science, they can do their best whether they like science or not. Additionally, they tried to read their textbooks again and again more than the non users from the results of the self-regulation inquire items. Therefore, it is clear that the students who have used the system for checking lessons have higher self-regulated learning strategies than the students who have not used it. It is unsure if the students who used Science Net already had high self-regulated strategy or the students who used the system improved their self-regulated strategy. Therefore, we want to carry out a thorough investigation of that in the future. 3.3 Performance of Science Table 5 indicates the results of the performance test for a year. The average of the users who have used it for checking lessons is 92.7, the average of the users who haven’t used it for checking lessons is 91.7, and the non users’ average is 91.1. We analyzed them by ANOVA, however, they have no significance because the averages of the students’ tests were high-scoring. Therefore when we analyze their performance of science, we need to

88

T. Kitazawa et al. / Science Net: Effects of an e-Learning System

check not only performance tests but also other materials such as worksheets, homework, and so on.

4. Conclusion This paper describes the assessment of our science e-learning site ‘Science Net’ which elementary school students have used it for one year. First, the findings show that it is not only being used it for checking lesson content but also for asking questions, lesson revision, and searching for science information via a bulletin board system (BBS). Second, the users who have used it for checking lessons tend to score higher in the self-regulated learning strategies than the non users from the results of the Motivated Strategies for Learning Questionnaire (Pintrich࡮De Groot 1990), in terms of 2 of the cognitive strategies (When I study for a test, I try to put together the information from class and from the book, and so on), and 4 of the self-regulations (I work on practice exercises and answer end of chapter questions even when I don’t have to; when I’m reading I stop once in a while and go over what I have read, and so on.). But, when we analyze their performance of science, we need to check not only performance tests but also other materials such as worksheets, homework, and so on. And then, it is unsure if the students who used Science Net already had high self-regulated strategy or the students who used the system improved their self-regulated strategy. It is important that the students have self-regulated learning strategies when they study with e-learning because they have the potential to dropout if they don’t have the strategies (Kougo & Nojima 2004b). Snowman (1986) suggested that the learning cycle which the students would have the self-regulated learning strategies as below; 1) setting up a goal, and analyzing the learning strategies, 2) designing the learning strategies, 3) implementing the learning strategies, 4) reflecting their learning outcome, 5) improving their learning strategies, and 6) checking all learning steps with their meta-cognitive strategies. We hope that the learning cycle will be considered for use in science classes with ‘Science Net’, and all elementary school students will use it for checking the science lessons in the future. Reference Kitazawa,T., Kato,H., Akahori,K. (2006) A Study of How Elementary School Students Acknowledged our Science e-Learning Site: The Effectiveness of the ‘Science Net’ as a form of Blended Learning, Journal of Science Education in Japan,30(1): pp.37-47 Kitazawa,T., Nagai,M., Kato,H., Akahori,K. (2005) Development and Assessment of the ‘Science Net’ e-Learning System for Science Education at the Elementary School Level, Proceedings of International Conference on Computers in Education 2005: pp.740-743 Kogo, C., Nojima, E. (2004a) Self-Regulated Learning of eLearning, Proceedings of the Japan Psychological Association the 68th Annual Meeting: p.1157 Kogo, C., Nojima, E. (2004b) Student Dropout in e-learning and its Symptom, Proceedings of the 20th Annual Conference of Japan Society for Educational Technology: pp.997-998 Pintrich & De Groot (1990) Motivational and Self-Regulated Learning Components of Classroom Academic Performance㧘 Journal of Educational Psychology, 82(1): pp.33-40 Programme for International Student Assessment (PISA 2000), Organization for Economic Cooperation and Development (OECD), http://www.pisa.oecd.org/ Snowman, J.: Learning tactics and strategies. In G.D. Phye & T. Andre (Eds.), Cognitive classroom learning: Understanding, thinking, and problem solving. Orlando,FL: Academic Press, 1986. Trends in International Mathematics and Science Study 2003 (TIMSS2003) of International Association for the Evaluation of Educational Achievement (IEA)(2003) Zimmerman,B.J. (1989) A Social Cognitive View of Self-Regulated Academic Learning, Journal of Educational Psychology, 81(3): pp329-339 Zimmerman,B.J.(1998) Developing self-fulfilling cycles of academic regulation: an analysis of exemplary instructional models. in D.H. Shunk & B.J.Zimmerman (Eds.), Self-regulated leaning, .New York: The Guilford press Zimmerman & Martinez-Pons (1990) Student Differences in Self-Regulated Learning: Relating Grade, Sex, and Giftedness to Self-Efficacy and Strategy Use, Journal of Educational Psychology, 82(1): pp.51-59

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

89

Using Satellite Resources for Scientific Inquiry Beaumie Kima, Manetta Calingerb, Debbie Denise Reeseb National Institute of Education, Nanyang Technological University, Singapore b Center for Educational Technologies, Wheeling Jesuit University, U. S. A. [email protected]

a

Abstract: NASA Learning Technologies is a development effort to create learning tools that provide access to NASA content and data in an engaging and dynamic manner. The present study is the initiation of the design research for one of those developed tools, which provides access to the satellite imagery and scientific visualizations on a 3D globe. NASA education’s challenge is not the incorporation of authentic high-tech resources. Rather, it is the preparation for educational use with well-defined science topics. The results of this study suggest that using this tool within a motivating context and an appropriate learning activity could provide positive impacts on science understanding and learning experiences. Keywords: Science education, inquiry, visualization, 3D, evaluation

Introduction The science education community is placing increasing emphasis on classroom practices of authentic scientific inquiry [e.g., 1]. Inquiry as a pedagogical strategy is adopted for learners to experience important aspects of science practice, which includes scientists’ use of modern science technology [2]. As one of the U.S. government’s most influential scientific organizations, the National Aeronautics and Space Administration (NASA) has an important mission of inspiring the next generation in science, technology, engineering, and mathematics careers. With a wealth of research resources and world-class facilities, NASA aims to provide unique learning opportunities of modern scientific inquiry to students and educators [3]. NASA’s Learning Technologies initiative is specifically focused on making such scientific resources usable and freely available to students and educators. This study initiated the design research for one of NASA’s advanced learning technology applications. 1. Perspectives: Doing and Understanding Science 1.1 Visualizations in Science Education What does it mean to do science and be a scientist? Despite the emphasis on enhancing students’ conceptions of the nature of science, research shows that K-12 students as well as their teachers do not have a complete understanding about them [4]. Learning environments with authentic inquiry activities support construction of students’ understanding of science practice and knowledge. The science technologies, such as scientific visualizations, have great potential to provide learners with meaningful experiences in science practice. As the use of technology in science is rapidly increasing, educational researchers also give attention to the potential affordances of emerging technology for inquiry-based pedagogies [5, 2]. Many science areas now use computational devices for their research to facilitate the collection and interpretation of massive data sets [6]. To provide learners with authentic experience as novice scientists, there is enterprise to develop learning tools that

90

B. Kim et al. / Using Satellite Resources for Scientific Inquiry

imitate the tools of scientists. This tool development is based on the belief that the learning of scientific content should be grounded in an understanding of the scientific investigation processes and an application to real problems [7]. The Learning Through Collaborative Visualization (CoVis) project, which incorporated weather visualizations, went through the iterations of research and development [8]. They studied the implementation of a six-week global warming curriculum during which middle/high school students prepared briefings for a fictitious international conference. The study showed that the CoVis project provided a coherent motivating context, but with the issue of time commitment for in-depth inquiry [2]. 1.2 Finding Meaningfulness Through Science Practice with NASA Learning Technologies Learners often establish relations within the learning context in regards to the personal importance or meaningfulness (e.g., the athletically talented may regard it important to excel in every physical education activity). Wenger [9] interprets learning as social participation. The participants initially learn by taking a secondary role in the practices; they gain meanings of their practices through the learning experience. The social learning context in the classroom and the recognition of learners’ roles as participants are significant for the performance of the learners. However, the sense of belonging beyond the local context is what makes the learning context even more authentic. The learning experience provides learners with images of the world, with a view of past and future possibilities and with a better perspective of their position in relation to the world [9]. NASA Learning Technologies is a research and development effort to create learning tools that provide access to NASA resources in an engaging and dynamic manner. NASA has created a set of time-series satellite imagery and data visualizations, called SVS (Scientific Visualization Studio) and an application that lets you dynamically browse, download, and display them on 3D globe, called World Wind. Spatial representations transform the world into images by symbolizing, changing scales, and taking perspectives (i.e., reference frames) [10]. SVS/World Wind not only incorporates composites of spatial properties, i.e., satellite imagery and scientific visualizations, in scientifically meaningful manner but also potentially makes them personally meaningful to the learners. 1.3 The Potentials and Problems Dewey [11] argued that the gist of thinking is what we suggest to others based on what we see in the world. An understanding about space helps us “see” spatial properties as evidence with which we can suggest certain patterns, changes, and/or possible solutions to problems. Some problem-based learning (PBL) approaches direct learners to scientific visualizations from scientific organizations to guide authentic inquiry (e.g., Exploring the Environment, ETE). PBL, however, does not always provide a good interface to examine visualizations. SVS/World Wind could provide learners opportunities to 1) engage in authentic inquiry with scientific visualizations in an interactive interface, 2) find global meanings of their classroom practice by using NASA science/technology and dynamically communicating with NASA data servers, and 3) gain meaningful understanding about spatial properties through dynamic 3D Earth perspectives. The purpose of this study was to examine the potential of this new way of presenting scientific visualizations for a positive impact on science understanding and learning experience. The four guiding questions are: 1. How well can SVS/World Wind support learning as a learner investigation tool? 2. What experience (troubles/successes) do learners have during activities? 3. What tradeoffs, if any, are there for using SVS/World Wind? Are they worthwhile? 4. How can SVS/World Wind better support learning (directions)?

91

B. Kim et al. / Using Satellite Resources for Scientific Inquiry

2. Methods 2.1 Conditions and Design The El Niño unit of the ETE was modified into two sets of five one-hour lessons. Both sets provided potentially motivating context for learners, learner-appropriate activities and data, and the support from the same instructor [6]. The scaffolding interface was the condition. Context: The El Niño 2005. Predicting the upcoming El Niño event addresses the real situation facing scientists at present and provides a motivating context for learners: In the summer 2005, El Niño may develop in the Pacific. Early spring indications showed it may bring the same detrimental effects as the 1997-98 El Niño. The Australia Predictive Ocean Atmospheric Model issued warnings on this possibility. The US National Oceanic and Atmospheric Administration (NOAA) and NASA experts do not expect such a severity.

Learners play the roles of NASA (treatment) or NOAA (control) scientists to investigate the El Niño data and to either confirm or refute the Australian prediction. Learner-Appropriate Activities and Data. To understand the patterns in climate conditions during a severe El Niño, learners investigated data from 1997-98 (sea surface temperature/height anomalies). They then examined the recent data up to early July 2005 to make their predictions on a possible 2005 El Niño. Scaffolding Interface. The treatment group used World Wind and its data browser to observe visualizations on 3D globe (Figure 1). The data browser displays time-series data as sequenced animated frames or individual frames. While viewing the data, learners can zoom in/out of places and rotate the Earth. The control group used the NOAA and other websites for data visualizations of sea surface temperature/height anomalies (Figure 2).

Figure 1. World Wind data visualization

Figure 2. Web-displayed 2D visualization

2.2 Instruments and Procedures The information from multiple sources addresses the four questions (see Table 1). The analysis of pre/post tests and learner products mainly informs the learner performance. Surveys and observations address the learners’ experience and engagement. The data would indicate the tradeoffs of and directions for using SVS/World Wind for learning. Learning and Performance. Pre/post tests had identical 18 multiple-choice items: the definition (2 items) and effects (6 items) of El Niño; background (4 items) and practical knowledge (3 items); and understanding scientific visualizations (3 items). Learners presented their predictions with evidence from visualizations. We examined the differences between two groups in 1) knowledge gains, and 2) the quality of their presentations. Table 1. Alignment Between Data Sources and Questions Pre-/Posttests Learner products Surveys Observations

1. Learning u u u u

2. Experience u u u

3. Tradeoffs u u u u

4. Directions u u u u

Experience and Engagement. The pre-survey included items on learners’ previous science learning experience, computer experience, and expectations about this activity. The

92

B. Kim et al. / Using Satellite Resources for Scientific Inquiry

post-survey asked about their science learning experience with technology compared to their expectations. They also filled out a daily student evaluation answering what they most and least liked about the day and one thing they learned. We examined 1) learners’ engagements, 2) partner interactions, and 3) the role of technology for their activities. Procedures. The participants were recruited from a five-day camp, in which students in grades 5-9 learned about space travel, extreme weather, and robotics. All 26 campers agreed to participate and were randomly assigned to the treatment (SVS, NASA) and the control (Webpage, NOAA) group 1 . One instructor administered both groups’ activities to control for teaching abilities. When one group was doing the El Niño activity, the other group had a different camp activity for an hour. Then the two groups switched activities. 3. Results 3.1 Overall Gains and Performance Knowledge Gains. A 2×2 mixed model repeated measures (condition×time) examined the effectiveness on knowledge gains. Four of the 26 participants did not complete pretest or posttest (two in each condition). The mean and standard deviation for the tests are in Table 2. The analysis showed a significant effect for time, F (1, 20) = 36.714, p < .001, Șpartial2 = .647. Neither the main effect nor the interaction (condition×time) was significant. We could not determine the differences in effectiveness on knowledge gain. The average gain for SVS group was 3.3, ranging from -1 to 7. The average for the webpage was 3.3, from -1 to 6. Table 2. Descriptive Statistics of Test Measures Pretest SVS (NASA) Webpage (NOAA)

Posttest

N

X

SD

X

SD

11 11

9.09 9.45

2.66 1.86

12.36 12.72

2.01 1.61

100%

Pre SVS

90%

Pre Webpage

80%

Post SVS Post Webpage

% Correct

70% 60% 50% 40% 30% 20% 10% 0% Definition

Effects

Background

Practical

Visualizations

Figure 3. Percentage of correct answers for categories of items A similar result was found for the item categories (see, Figure 3). Using the 2×2 repeated measures design, three categories showed significant effects for time: El Niño effects (6 items), F (1, 20) = 10.198, p = .005, Șpartial2 = .338; background knowledge (4 items), F (1, 20) = 18.404, p < .001, Șpartial2 = .479; and practical knowledge (3 items), F (1, 20) = 11.575, p = .003, Șpartial2 = .367. The interactions (condition×time) were not significant and there is no evidence for the differences in gains in any category between the two groups. 1

The groups were called NASA and NOAA to provide a realistic/motivating context. They will be referred as SVS and Webpage in discussion of the findings as the results do not indicate differences in the two organizations.

B. Kim et al. / Using Satellite Resources for Scientific Inquiry

93

The gains on the definition of El Niño (2 items) and analyzing visualizations (3 items) were not significant for either group. Although statistically insignificant, Webpage group might have gained more understanding for the background knowledge category. We expected the Webpage group might benefit from the more focused activities. They may have had less distraction within the interface and more involvement in reading through the websites. Naïve Exploration with 3D Visualizations. The gains seem to be positively related to the participants’ control of the visualizations. The Webpage participants’ gain scores were highly consistent with observation. Persons leading the team or looked enthusiastic gained more. The four participants from the Webpage group who had less control over and/or were less engaged in their activities gained only between -1 and 1 from the pretest to the posttest. Some SVS participants showed extreme exceptions. A participant (Rob) who was always quietly engaged in the activity had -1 gain score, and his partner (Carl) gained only 2 points. A participant (Alan) who expressed his excitement of using the software and looked most engaged in the investigation had -1 gain score. These cases could not be explained by our remote observations, but their presentations revealed how they went awry with their investigations. The data visualizations of 1997-98 El Niño indicators provided only the slice of data from the Pacific Ocean between 20 degrees north and south (see, Figure 1). This focused the learners’ attention to the area that was relevant to El Niño patterns and effects. When examining 2005 data, which had data for the entire Earth, these two teams seemed to have gotten lost. Rob and Carl claimed that Figure 4 demonstrated a similar abnormality of sea surface temperature as that of 1997-98 data, in which data from the Atlantic Ocean was excluded. Likewise, Alan and Luke explained their prediction of the severity of El Niño by showing the abnormal sea surface temperature of the North Atlantic Ocean (Figure 5).

Figure 4. Screen capture by Rob and Carl

Figure 5. Screen capture by Alan and Luke

Reporting Scientific Explanations. There were qualitative differences between the two groups in reporting their observations. SVS data visualizations are accessed as events, such as El Niño 1997-98, and presented as animations. The Webpage group used a website with static visualizations, with which they had to find a certain day of a month one by one to examine them. Resulting from this difference, most of the SVS group participants broadly discussed data visualizations, using the phrases as the previous El Niño, 1997-98 El Niño, current conditions, this year, etc. Webpage participants, however, provided at least the month and the year for the visualization, which showed on the downloaded graphics. Another difference was in how the background information about El Niño and its effects were reported. A majority of Webpage participants talked about why people worry about having a severe El Niño. This information comes from the handouts/websites, not from the data. One team concluded its presentation with a list of expected El Niño effects without any claim and evidence from the data. SVS participants, however, placed less stress on the additional background and more on the observed patterns of the visualizations. 3.2 Learner Experience The SVS group gave more positive responses for post-survey. Examining the sum of 17 items (out of 29) directly related to learner contentment (alpha = .94), there was a significant difference between the two: t(20) = 2.41, p= .026. The learning with SVS ( X =62.82) provided more positive learning experience than static visualization ( X =49.55).

94

B. Kim et al. / Using Satellite Resources for Scientific Inquiry

A. Overall Learning Activity. Both groups had somewhat positive experience, and some participants talked about this activity with their parents. However, not many of them learned more about El Niño outside of the camp. SVS group felt as if having authentic experience more than Webpage group. The differences were significant for item A4, “I felt as if I was a scientist while participating in the El Niño activity,” t (19) = 2.41, p = .027, and item A9, “I enjoyed presenting our discoveries of our research to others,” t (19) = 2.10, p = .049. Both groups seemed to enjoy predicting the next El Niño and feel like real scientists. B. Use of Scientific Visualizations. Regarding the use of scientific visualizations, the SVS group responded more positively. A significant difference was for item B1, “The science/technology helped me understand about El Niño better,” t (20) = 2.11, p = .049. SVS participants seemed to think their learning experience was enriched and exciting more than Webpage participants. When asked what they liked most about the learning activity, one SVS participant said, “I liked spinning the globe, using it to see the patterns of El Niño,” and another said, “It was different from what I had experienced before, and I enjoyed working in a different way.” Another significant difference was identified by item B3, “Looking at the visualizations, I argued with my partner to agree on what was going on,” t (20) = 2.50, p = .021. This result was consistent with our observations. For the SVS group there were discussions about visualizations among partners within each team as well as among different teams. There was only one Webpage team in which one member was vocal and provoked more conversations among the team members. C. Working with Others. Both groups of participants seemed to enjoy their experience with their partners. There, however, was a stronger pull toward working alone by the SVS group as exposed in item C6, “I prefer working by myself,” t (20) = 3.28, p < .01. The SVS participants who were engaged in and learned more from the activities seemed to consider their partner as a distraction. In the open-ended items, SVS group had more complaints about the lack of partners’ cooperation. Some of the SVS participants still preferred working by themselves even without any problem with partners. With an additional test on the knowledge gain and the item C6, “I prefer working by myself,” the significant correlation was found for the SVS group: F (1, 9) = 7.586, p = .022. There was no significance on this test for Webpage group. Interestingly, SVS group still enjoyed some pair-work. Item C3, “I enjoyed preparing the presentation with others,” showed a significant difference between the two groups: t (20) = 2.81, p = .011. In the open-ended survey items, eight SVS participants expressed a preference for presentations, and none mentioned a dislike. Six Webpage participants mentioned their enjoyment in the presentations, and three least enjoyed this aspect of the activity. Both groups wanted more preparation time. 4. Discussion The results suggest that using SVS/World Wind within a motivating context and an appropriate learning activity could provide positive impacts on science understanding and learning experience. There seem to be some tradeoffs in adopting this learning technology. Tradeoff 1: Naïve Explorations. The participants’ lack of foundation in geography and Earth science knowledge seemed to have a greater impact when there was more opportunity to explore. While investigating El Niño indicators, several participants of both groups had trouble locating South America. Some Webpage group participants could find the area only when they could see the expanded red on the map for sea temperatures during the 1997-98 El Niño. This was not a problem with SVS group for the 1997-98 data because only the relevant area was shown on the visualization. When examining the development pattern of 2005 El Niño, SVS participants were exposed to the data for the entire Earth. In the cases of Rob, Carl, Alan, and Luke in the SVS group, they looked for highly anomalous data from any place on Earth for the indicator of the El Niño development. This tendency of

B. Kim et al. / Using Satellite Resources for Scientific Inquiry

95

SVS participants could be easily detected based on how they presented the evidence with SVS visualizations (see, Figures 4 and 5). This would not have been revealed from Webpage participants’ presentations because they do not decide their visualization views. The lack of geography knowledge led some SVS participants to provide incorrect evidence. However, SVS group participants seemed to take more ownership of what they discovered from those data sets. At the same time, World Wind’s 3D globe interface made the participants’ need of geography knowledge more explicit to themselves, their peers (the presentation audience pointed this out), and the teacher. This should give learners more opportunities to address such needs when used the software for an extended period. Tradeoff 2: Reporting Scientific Explanations. By examining the animated sequence of data visualizations, SVS participants seemed to get a better grasp of its changing pattern than Webpage participants who had to examine one page at a time. In their presentations, however, SVS participants used a capture of one day’s data visualization but treating it as a representation of the whole period of the 1997-98 El Niño or the first half of 2005. Even if the instructor specifically pointed out several times how to find the date information, they seemed to affix to the idea that they were examining a period. This is definitely a tradeoff between two conditions in terms of getting the bigger picture versus paying attention to the data details. Both insights are essential for scientific investigations. There seems to be a relationship among the Webpage participants’ reporting of background El Niño information, their trend of more gain in background knowledge category, and time constraints of this study. Most SVS participants focused on exploring with SVS El Niño indicators and then had enough time to collect appropriate visualizations for their claims. Webpage participants seemed to have time to look up some information. A SVS team who took time to discuss other information, however, could not finish its investigation with the second dataset. Tradeoff 3: Engagement vs. Distraction. SVS participants felt more engaged in authentic science practice. One of the SVS participants commented about SVS/World Wind, “I think it would help meteorologists predict weather better in the future.” The 3D globe interface, however, is not the typical way that scientists work with data visualizations. The initial excitement of the participants could be the novelty effect with a 3D globe interface, but their feeling of authentic engagement could stem more from manipulating their view, controlling the animated sequences, and therefore, gaining more ownership over the colleted data. This is somewhat evidenced by the significant difference in the item B3, “Looking at the visualization, I argued with my partner to agree on what was going on,” in that each team member perhaps found his or her own observations meaningful to them. This provides a possible explanation for this item and the relevant observations (i.e., only one team with a vocal member in Webpage group had active conversations): Opportunities to explore with 3D globe provoked more learner discussions for the SVS group. This leaves us with an exciting potential for supporting authentic scientific process. Scholars of science education see argumentation as a core scientist activity [e.g., 12, 13, 14, 15]. With the current implementation, we cannot say if learners were engaged in any scientific argumentation. The future implementation should consider this scientific practice. Distracted behaviors were shown by both groups (i.e., playing games, changing background, etc.). For the SVS participants, the 3D interface provided another source of distraction, such as zooming in/out on familiar locations, spining the globe, or exploring other SVS data. Such behaviors seemed to be related to their attitude toward the learning. The engaged participants paid some attention to such behaviors of others, follow it for a short period of time, and then return to their tasks. 5. Conclusions Visualizations available from scientific organizations offer great potential for learning.

96

B. Kim et al. / Using Satellite Resources for Scientific Inquiry

SVS/World Wind has the capacity to enable learners to meaningfully interpret science resources. The challenge is not in the incorporation of authentic, high-tech resources but in the preparation for educational use with well-defined topics. U.S. Department of Education [16] pointed out that teachers are now left with an abundance of resources without sufficient understanding of how to use them effectively to enhance learning. Without providing any guides to teachers, the great potential afforded by learning technologies might be wasted. With enhancements, SVS could strongly support such lesson plans for science education. For example, with baseline data (e.g., sea surface temperature anomaly for mild El Niño years) for comparison to event-based data (e.g., sea surface temperature anomaly during 1997-98 severe El Niño), learners would better understand the meaning of data. It is also essential to focus the categories of data sets to the topics that are most essential for K-12 science education and provide constant updates and additions to such collections. This study was intended to be formative and small-scale as we conducted an initial exploration of the instructional potential of SVS/World Wind. We could not find any difference in the effectiveness in participants’ knowledge gain between using SVS/World Wind and using the simple web interface. We expect better outcomes for future studies on long-term effects with enhanced learning technology and appropriate curricula. References [1] [2] [3] [4]

[5]

[6] [7]

[8]

[9] [10]

[11] [12] [13] [14] [15] [16]

National Research Council. (1996). National science education standards. Washington, DC: National Academies Press. Edelson, D. C., Gordin, D. N., & Pea, R. D. (1999). Addressing the challenges of inquiry-based learning through technology and curriculum design. Journal of the Learning Sciences, 8(3/4), 391-450. National Aeronautics and Space Administration. (2003). Education enterprise strategy (No. NP-2003-10-322-HQ). Washington, DC: NASA Headquarters. Lederman, N. G., Abd-El-Khalick, F., Bell, R. L., & Schwartz, R. S. (2002). Views of nature of science questionnaire: Toward valid and meaningful assessment of learners' conceptions of nature of science. Journal of Research in Science Teaching, 39(9), 497-521. Brown, A. L., Ash, D., Rutherford, M., Nakagawa, K., Gordon, A., & Campione, J. C. (1993). Distributed expertise in the classroom. In G. Salomon (Ed.), Distributed cognitions (pp. 188-228). New York: Cambridge University Press. Edelson, D. C., & Gordin, D. (1998). Visualization for learners: A framework for adapting scientists' tools. Computers & Geosciences, 24(7), 607-616. Loh, B., Reiser, B. J., Radinsky, J., Edelson, D. C., Gomez, L. M., & Marshall, S. (2001). Developing reflective inquiry practices. In K. Crowley, C. D. Schunn & T. Okada (Eds.), Designing for science (pp. 279-323). Mahwah, NJ: Lawrence Erlbaum. Edelson, D. C., Pea, R. D., & Gomez., L. (1996). Constructivism in the collaboratory. In B. G. Wilson (Ed.), Constructivist learning environments: Case studies in instructional design (pp. 151-164). Englewood Cliffs, NJ: Educational Technology Publications. Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. Cambridge, MA: Cambridge University Press. Liben, L. S., & Downs, R. M. (1991). The role of graphic representations in understanding the world. In R. M. Downs, L. S. Liben & D. S. Palermo (Eds.), Visions of aesthetics, the environment & development (pp. 139-180). Hillsdale, NJ: Lawrence Erlbaum. Dewey, J. (1910/1997). How we think. Mineola, NY: Dove. Bell, P., & Linn, M. C. (2000). Scientific arguments as learning artifacts: Designing for learning from the web with kie. International Journal of Science Education, 22(8), 797-817. Driver, R., Newton, P., & Osborne, J. (2000). Establishing the norms of scientific argumentation in classrooms. Science Education, 84(3), 287-312. Kuhn, D. (1993). Science argument: Implications for teaching and learning scientific thinking. Science Education, 77(3), 319-337. Nussbaum, E. M. (2005). The effect of goal instructions and need for cognition on interactive argumentation. Contemporary Educational Psychology, 30(3), 286-313. U.S. Department of Education. (2004). Toward a new golden age in American education (National Education Technology Plan 2004). Jessup, MD: U.S. Department of Education.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

97

Experimental Researches on Development of Pupils’ Advanced Cognitions in PRIME Environments Zhou Yueliang1, Wang Lianghui1, Lin Xiuqin2 School of education, Zhejiang Normal University, China 2 Knowledge Science and Engineering Institute, Beijing Normal University, China 1

Abstract: ICT application in education has become one of the most attractive topic discussed in modern educational theory and practice. This paper try to verify the effect of ICT application in education, especially in development of students’ advanced cognitive abilities, by confirmative experiments in online learning support system named PRIME. After data analysis, the paper gets the conclusions that the PRIME improves students’ learning achievement by affect the students’ advanced cognitive abilities, and the students’ advanced cognitive abilities, as intermediate variables, play very important roles on the performances of students, where PRIME affects their learning. Keywords: Advanced Cognition; PRIME; Virtual learning Environments (VLEs);

subject learning strategy (SLS). 1. Introduction Recently, the application of ICT (Information and Communication Technologies) in education has become one of the most attractive topic discussed in modren educational theory and practice, and the countries all over the world have paid much attentions to it because it brings many new features of learning and teaching. As researches show, the mastery over the skill of online learning has close relationship with the advanced cognition, especially the self-cognition ability (Hong, McGee & Howard, 2000). Also, learning under ICT environments requires the learners of more independence and self-control, being called them advanced cognitive abilities. In fact, the relationships between ICT and the development of advanced cognition was paid much attention in late 1990s. Some people focused on the influence of the advanced cognition on learning in ICT environment, e.g. Hill & Hannafin˄1997˅, Hill(1999) , Jegede(1999) , Aleven & Koedinger(2002). Some studied on the cultivation of advanced cognition by ICT, e.g. Collins & Brown(1991), Herrington & Oliver(1999,2000), Lin(2001). Tools aimed at advanced cognition had also been developed, e.g. CSILE/KnowledgeForumˈSpeakEasy (Hsi & Hoadley, 1997), Belvedere (Cavalli-Sforza, Weiner, et al, 1994), Convince Me (Schank, 1995), Euclid (Smolensky, Fox, et al, 1988)ˈand SenseMake˄Bellˈ1997˅etc. All these researches mentioned that the support of the society is very important to the construction of learners’ knowledge, especially the acquisition of advacned cognition. In the paper, a VLEs named PRIME of cultivating the student’s advanced cognition is introduced. It has five key functions (PRIME is the abbreviation from all the functions’ first letter): a) the Process and Production of teaching and learning; b) the plan, summary,

98

Z. Yueliang et al. / Experimental Researches on Development of Pupils’ Advanced Cognitions

and Reflection of the learning and teaching; c) Interaction between the learning and teaching; d) Multilevel collaboration; e) Educational administration function. Advanced cognition is the most important variable in this research, but the concept of advanced cognition is very vague. In The Modularity of Mind, Fodor (1983) pointed out that the process of mind has two kinds: the advanced and the lowed, corresponding input system and central system in human’s information process. The central system referred to advanced cognitive processes includes the form of a belief, thought, reasoning, problem solving etc. Apparently, this research cannot include all advanced cognitive abilities. So we mainly focus on some important and typical advanced cognitions such as cognitive strategy ( or problem-solving strategies), self-regulatation (self-control) and reflection. They have a large impact on learning in ICT environments. Further, because ICT is the medium in the case, the student ICT literacy is included as a part of advanced cognition. 2. Research methods 2.1 Participants All the Participants are students from two elementary schools. One is in Shunde, Guangdong province and the other is from Zhuji, Zhejiang province. Each school participants are divided into two groups. One group students learn on the PRIME and their records of online activities will be analysed as independent variables. The students will use the VLEs and the relationship between advanced cognition and utilization of PRIME without the interferance of teaching will be explored. The other group is the same with the group except for they were interfered by the teaching and experiment. There are more than 1200 Participants altogether. The structures of the paticipants are showed in Table1. Table 1. The distribution and constitution of the participants school

Shunde

Zhuji

Types of class

Grade 3

Grade 4

Grade 5

Boy

Girl

Boy

Girl

Boy

Girl

Experimental

30

15

46

36

43

Comparative

26

19

22

16

Experimental

48

26

23

Comparative

33

34

22

Grade 6 Boy

Girl

Total Boy

Girl

35

119

86

25

17

73

54

15

25

15

17

19

113

75

18

44

35

50

23

149

110

2.2 Research Design The research is focus on two problems delt with by two experiments: experiment 1 (E1) and experiment 2 (E2). The E1 is focus on whether PRIME will facilitate the development of advanced cognition with no teaching interference. It takes the records of students’ online activities as the independent variables and the advanced cognitions as dependent variables. The E2 mainly focuses on whether the new teaching model using PRIME improve the students’ learning strategies. It takes the teaching model as the independent variables and the learning strategies as dependent variables The two experiments lasts for 2 years and proceding seperately. Concerning about the launch time of expriments, the E1 is half a year earlier than the E2. During the half year, paticipants were instructed to be familiar with PRIME and acquire ICT performances and it can make E2 smooth. Learners’ achievements (reading & writing, mathmatics, science and information technology (IT))

Z. Yueliang et al. / Experimental Researches on Development of Pupils’ Advanced Cognitions

99

are served as indexes of development of advanced cognition and effects of PRIME. All students’ ICT operating abilities are enough to accomplish the experiment, avoiding bias due to different abilities ICT. Participants in E2 was showed in Table 2. Table 2. Disposition of Experimental Group (E2) School

Shunde

Dis of exp Reading Writing

Math

Zhuji Science

IT

Reading Writing

Math

IT

Grade3(5) Grade5(1) Grade4(1) Grade4(1) Grade3(5) Grade3(8) Grade5(6) Grade3(8) Grade3(8) Grade5(1) Grade5(2) Grade4(2) Grade4(2) Grade4(1) Grade3(9) Grade4(7) Grade3(9) Grade3(9)

classes

Grade4(2) Grade4(6)

Grade4(6) Grade4(6)

Grade5(1)

Grade5(6) Grade5(6)

Grade5(2)

Grade6(7) Grade6(7)

2.3 Research Procedure The experiments in two schools are proceeding seperately with same start and finish time. The E1 and E2’s process of research are differed. E1 began with the official launch of PRIME and lasted for one year. Students were asked to do various activities on PRIME such as writing Blog, sharing resources, commenting works of each other, and etc. These activities were recorded in the form of E-portfolio. Compared with the E1, E2 procedure is more complex and strict. Four phases and control means are included: (1) Exploration of new teaching models: Researchers and teachers together discuss how to utilize PRIME in classroom and finalize some new teaching models based on PRIME. Every model is made certain by four process: design, teaching trial, amending and rechecking. Reading and writing teaching model will be discussed in this part. (2)Pre-test: including tests of all scores of the subjects, GOALS and questionnaires of students’ learning strategies. The pre-test ensure rough equal of learning, cognition and metacogtnition between the experimental class and comparative class. (3) Formal experiment: Teachers use the model to teach designed in the first phrase and the time lasts for 2 months. (4) Post-test: things that tested in this period is the same with the privious test. At last, all data are processed using SPSS11.5. Reading is very important abilities in elementary school. In the designed reading model within PRIME, the teacher analyse the task of a reading and decide what materials and questions to present to the students. After all the materials needed are prepared by teacher and students, teacher will use PRIME tools to unify and refine the materials and make them in hyper-text. Then teacher uploads all the things together on the internet and studets can download and browse them. The details of the model can be found in Figure 1.

Figure 1. Reading teaching on PRIME

Figure 2. Writing teaching on PRIME

Writing is a difficulty for elementary school teaching. Students have limited living circle and few experiences. Many of them are in defect of headspring and desire for

100

Z. Yueliang et al. / Experimental Researches on Development of Pupils’ Advanced Cognitions

writing. PRIME gives them a good place exchanging everyone’s ideas andexperiences and inspire students’ interestings using web publishing. The details can be seen in Figure 2. 2.4 Testing Tools Experimental results evaluated mainly from learning achievement, cognitive and meta-cognitive strategies, motivation and goal setting strategies and subject learning strategies (SLS). Learning achievement adopts the means of school examination and evaluation of homework, here mainly introduce tools to test the other variable. GOALS is abbreviation of the Goal Orientation and Learning Strategies Survey, created by Martin Dowson in his dissertation. GOALS’ structure and subscales as follows (Dowson,2003) : a) academic goals; b) social goals; c) cognitive strategies; d) cognitive strategy; e) additional questionnaires. The reliability of origin GOALS subscales is between 0.72~0.92 and Cronbach Į coefficient of Chinese version is 0.920. That’s to say, the revised questionnaire has good adaptation to Chinese schools and students. Four subjects are included in the experiments: Chinese(reading & writing), mathematics, science and ICT. Subject learning strategy (SLS) questionnaire is mainly used to examine advanced cognitions, such as reading skills, writing skills, math problem-solving strategies and ICT literacy. It has five subscales. With the exception of four subjects subscales, it also contains a special subscale of meta-cognition. Its Cronbach Į coefficient is 0.903. 3. Results and Discuss Two kinds of experiments brought forth two kinds of data. One is online activities records of participants on PRIME, which also can be called active indexes. Active indexes shows students’ visible behaviors on PRIME, such as online time, messages posted, resource or works uploaded, and so on. By analyzing the relationship between active indexes and development of advanced cognition, which community behaviors helping to students’ cognition can be recognized. It can help us design experiments to inspire students to do more contributing community behaviors, which can accelerate their academic performance. The other is the comparative data acquired by teaching interference with the pilot group and its counterpart, such as academic achievement, GOALS scores, result of pre-test and post-test of SLS. By analyzing them, we can understand effects of teaching model on PRIME, also examining the functions of PRIME to advanced cognitive and academic performance. 3.1 Relationships between Active Indexes and Advanced Cognitions The active indexes come into being from March 28th, 2004 to April 1st, 2005. They shows students’ activities in PRIME during this time. This analysis on active indexes prescinds from quality of class which students belong to. Its purpose is to get a general understanding about students’ using of PRIME. With a view to disbenefit impact on statistics result, we made a necessary revise on ultra data of active indexes. Table 3 is about the correlations between active indexes and GOALS post-test. Login times, accumulative online time and the two indexes of sociality which is called social approval and social status all have come forth negtive correlation. Besides them, the number of messages has a obvious relationship with general cognition ,and the number of post also has a obvious relationship with cognition of social status, general cognition and

Z. Yueliang et al. / Experimental Researches on Development of Pupils’ Advanced Cognitions

101

academic support. Table 3: Correlations between active indexes and items in GOALS post-test

Table 4 shows except two active indexes called online time and comments, the rest five active indexes have significant relationships with the total scores of SLS. All seven indexes have significant relationship with ICT, and the number of messages has significant relationship with the scores of reading and writing. Oppositely, accumulative online time has negtive correlation with SLS. This negtive correlation reflects people’s about students’ learning freely online indirectly, which makes tremendous contrast with positive effects caused by the development of advanced cognitive ability. It tells that people’s opinions of ICT should be altered urgently! Generally speaking, active indexes has close relationship with GOALS’ scores and SLS scores. Besides, correlation between active indexes and ICT literacy is very remarkable. Table 4: Correlations between active indexes and SLS

3.2 Relations between Teaching methods in PRIME and Advanced Cognition Developing 3.2.1

Relation between the Teaching Intervention in PRIME and GOALS Scores

Using the multivariate analysis of variance methods to compare GOALS pre-test and post-test scores of control group with experiment group, on GOALS total scores, quality of class (experiment or control group) main affect(F=5.507ˈp=0.021˅ˈgender main affect(F=7.939ˈp=0.006˅ˈclass and gender interaction˄F=4.298ˈp=0.041˅are all significant. It shows that PRIME applications in subjects teaching, gender and their interaction make prominent effects on GOALS total scores. But on subscale scores, these three variables manifest different effects: class quality makes notable effects on mastery

102

Z. Yueliang et al. / Experimental Researches on Development of Pupils’ Advanced Cognitions

goals, society conformity, social affiliation, sense of belonging, while notable main effect on work avoidance, social responsibility, social concern, common strategy˄F=7.933ˈ p=0.006˅, modulation ˄F=4.466ˈp=0.037˅, general cognitive˄F=6.746ˈp=0.011˅, common motivation, sense of belonging. They indicate that PRIME has different effects on boys and girls. It seems that by dint of PRIME to teach students can enhance schoolboys’ cognitive ability more efficiently. Certainly, application of PRIME in teaching has prominent effects on students’ general advanced cognitions, even brings more prominent social effect, such as sense of belonging, motive, etc. The result hints that cultivating methods of advanced cognition by PRIME may highly depend on social communication and community online. It is identical with the cultivation methods of metacognitition mentioned in Lin’s studies (Linˈ2001). 3.2.2

Relation between Teaching Intervention in PRIME and Development of SLS

To SLS, only Grade 4 pupils are involved in experiment. The result manifests that the total score of SLS make significant affects in both group˄F=8.044ˈp=.005˅, but pretest-posttest difference is insignificant; class quality has a prominent main affect on the interaction of pretest-posttest, but has little effect on subscales, which may due to the limited time btween pretest and posttest. The result of another same kind of experiment adopting pretest-posttest experimental design shows that sex difference has little effect on the SLS, but grade difference and class quality make very prominent effect on every development of SLS. Significant level of most items are lower than 0.01 (Table 5). Hence teaching models based on PRIME can promote students’ SLS efficiently. Table 5: Multivariate analysis of variance of SLS

3.2.3

Relations among PRIME, Advanced Cognition, and Students’ Performances

This part will discuss relationships among the application of PRIME, advanced cognition and learning performances so we can have a better understanding of PRIME, in which ICT affects performances. Table 6 shows the correlations between SLS and achievements of participants. From the scores students achieved in two midsemester on a whole, students from both classes can achieve a higher score as long as they have a better learning skill, and also some other

Z. Yueliang et al. / Experimental Researches on Development of Pupils’ Advanced Cognitions

103

factors, such ICT literacy, there being a close relationship between teaching and the development of strategies within PRIME. Anyway, there is still something left for us to explore. For example, in the first semester in 2004, the scores students achieved in Chinese are irrelevant to reading and coordinating skills, and some conditions exist in Math examination for the same semester. Table 6 :The correlation between achievement and SLS

Table 7 adopts multianalysis to analyze the relationships between active indexes and the final exams of the students in Zhuji Experimental School in the first semester, 2004. The result shows that grade, sex, experimental intervention cause different effects on different subjects. It manifests the factors which influence the students’ learning are very complicated in the process of the application of PRIME. Table 7: Analysis on the effect of the application of PRIME on achievement

4. Conclusion This research is according to the theory and practice related to the training of advanced cognition, and adopt education experiment to explore the education application of PRIME and the relation between learning achievement based on PRIME and the primary pupil’s advanced cognition, and also verify the effect of PRIME.

104

Z. Yueliang et al. / Experimental Researches on Development of Pupils’ Advanced Cognitions

From the data analysis, there is significant correlation between students using PRIME and their advanced cognitive ability, which include general metacognition and planning ability, SLS and ICT literacy. But the negative relative between some society indexes and use of PRIME shows that some improper conception of ICT may affect the use of PRIME, and more attentions should be paid on those problems. Although, We cann’t reach the conclusion from the analysis of statistics results that the PRIME can improve students’ learning achievement by affect the students’ advanced cognitive ability. It is evident that there are distinct relationships among the learning achievement, scores of GOALS and SLS, and PRIME plays an active role in the development of the SLS and the GOALS as well. Therefore, we can accept what we have presumed in the very beginning of this paper, that is, advanced cognitions, as an intermediate variable, plays an important role in a system where PRIME affects learning. Of Course, in order to collect more convinced data, further researches should be carried out.

References [1] American Association of School Librarians ˈ Association for Educational Communications and Technology. (1998) Information Literacy Standards For Student Learning: Standards And Indicators. [2] Aleven,V.A.W.M.M. &. Koedinger, K.R. (2002) An effective metacognitive strategy: learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26: 147–179. [3] Bell, P. (1997) Using argument representations to make thinking visible for individuals and groups. In Proc. Computer Supported Collaborative Learning ‘97, University of Toronto˖10-19. [4] Bell, P., Davis, E. A., & Linn, M. C. (1995) The knowledge integration environment: Theory and design. In Proceedings of the Computer Supported Collaborative Learning Conference ‘95. Mahwah, NJ: LEA: 14-21. [5] Collins, A., Brown, J. S., & Holum, A. (1991) Cognitive apprenticeship: Making thinking visible. American Educator, 15 (3), 6-11, 38-46. [6] DowsonˈM.(2003) Relations Between Students' Academic Motivation, Cognition and Achievement in School Settings. Doctorial DissertationˈUniversity of Western Sydney. [7] Herrington, J., & Oliver, R. (1999) Using Situated Learning and Multimedia to Investigate Higher-Order Thinking. Journal of Interactive Learning Research, 10(1): 3-24. [8] Herrington, J. & Oliver, R. (2000) An instructional design framework for authentic learning environments. Educational Technology Research and Development, 48(3), 23-48. [9] Hill, J. R. & Hannafin, M. J. (1997) Cognitive strategies and learning from the World Wide Web. Educational Technology, Research and Development, 45(4): 37~64. [10] Hill, J. R. (1999) A Conceptual Framework for Understanding Information Seeking in Open-Ended Information Systems. Educational Technology, Research and Development, 47(1): 5~27. [11] Hong, N. S., McGee, S. & Howard, B. C. (2000) The Effect of Multimedia Learning Environments On Well-Structured and Ill-Structured Problem-Solving Skills. American Educational Research Association 2000, New Orleans. [12] International Society for Technology in Education. (ISTE, 1998) National Educational Technology Standards for Students. International Society for Technology in Education (ISTE), NETS Project. Available at http://www.iste.org. (Accessed 21 Nov, 2004) [13] Jegede, O., Taplin, M., Fan, R. K.; Chan, M. C. & Yum, J. (1999) Differences between low and high achieving distance learners in locus of control and Metacognition. Distance Education, 20 (2): 255-273. [14] LinˈX. D., Hmelo, C., Kinzer, C. K. & Secules, T. J. (1999) Designing technology to support reflection, Educational Technology, Research and Development; 47(3):43~62. [15] Waxman, H. C., Lin, M. F. & Michko, G. M. (2003) A Meta-Analysis of the Effectivenesss of Teaching and Learning With Technology on Student Outcomes. Learning Point Associates. [16] Pi Liansheng. (2000). Instruction DesignüüTheory and Technology of Psychology (In Chinese). Beijing: Higher Education Publishing House: 43. [17] Wang zhenhongˈLiu Ping. (2000) MotivationǃLearning Strategy and Intelligent Level Affect Students’ Achievement (In Chinese). Acta Psychologica Sinica, 32(1): 65-69.

ITS, et al.

This page intentionally left blank

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

107

Teaching Chinese Handwriting by Automatic Feedback and Analysis for Incorrect Stroke Sequence and Stroke Production Errors Kai-Tai Tang and Howard Leung Department of Computer Science, City University of Hong Kong, Hong Kong SAR [email protected] Abstract: Writing Chinese character is not trivial and people often commit stroke sequence error and stroke production errors. In this paper, we propose a web-based education system which allows users to practice Chinese handwriting freely. An Automatic Feedback and Analysis (AFA) tool is introduced to the system which can automatically check both the stroke sequence errors and stroke production errors by analyzing the online data of the learner’s input character. Feedback will be provided if the learner commits errors in the stroke sequence and/or during stroke production. Experimental results demonstrated that our method can check multiple handwriting errors with encouraging accuracy. User studies showed that the learning time is shorter if our proposed system is used. Keywords: Chinese handwriting education, stroke production, stroke sequence, stroke matching, web-based application, pattern recognition, stroke analysis, automatic feedback

Introduction Chinese character is a kind of ideogram composed of strokes with different lengths, shapes and orientations. The generally accepted stroke sequence can be looked up from the Chinese dictionary. Shimomura [1] suggested that the standard stroke sequence was designed for saving energy in writing and easy memorization. Law et al [2] have studied the handwriting ability of junior primary school students and they found that the children often fail to master the stroke sequence and stroke production. Stroke sequence error is committed if one writes a character with the wrong order of strokes. Stroke production error is committed if the handwriting contains (1) concatenated stroke, (2) broken stroke, (3) missing stroke, and/or (4) extra stroke. In a Chinese dictionary, the characters are indexed by either radicals (components of a character) or number of strokes. In order to learn Chinese efficiently, students should correct their handwriting errors first. Otherwise, they may wrongly recognize a radical or realize the wrong number of strokes of a character hence they are unable to lookup a character from the Chinese dictionary. Teacher assessment and student self-correction are two possible ways to correct Chinese handwriting errors. Teachers can point out the mistakes in the students’ handwritings and give them appropriate advices. Student can also compare his/her handwriting with the template to verify that there is no handwriting error. Caroline [8] emphasized that regular feedback can motivate the student in language learning. However, it is impossible for a teacher to be available for every student in a time-limited lesson and a student may not be able to spot his/her own errors through self-correction. There is thus a need to develop an intelligent tool that can automatically verify students’ handwriting and

108

K.-T. Tang and H. Leung / Teaching Chinese Handwriting by Automatic Feedback

give feedbacks to them in order to enhance their learning. This will also ease the workload of the teachers. There are three main streams of Chinese handwriting education systems. The systems in the first stream teach students to write properly [3][4][5][6][7]. Systems in [3][4][5] teach stroke sequence while [6][7] can verify whether the input character and the template character look alike. The second stream focuses on training users to write Chinese beautifully [6][8]. The third stream aims to improve the graphomotor skills such as writing speed and control of hands [7][9]. There are some drawbacks with existing systems. The system proposed in [3] is read-only and users cannot practice handwriting through the system. The systems proposed in [4] and [7] allow users to practice handwriting in a guided manner so they are not able to write freely. Tsang and Leung [5] proposed a PDA education tool that allows users to write freely and can verify the input stroke sequence. The system proposed by Tan et al. [6] also allows students to write freely and it can verify more handwriting problems such as number of strokes and relative stroke lengths. However, it is not flexible and generic enough because it is character dependent such that every time a new character is added, specific threshold values for that character should be set. The system proposed in [6] can only give feedback on one kind of handwriting error even though the learner has committed multiple handwriting errors. To relax the restrictions and limitations of existing systems, it motivates us to propose a system that allows users to practice handwriting freely, and checks both the stroke sequence error and stroke production errors in the input handwriting simultaneously. In our proposed system, users are allowed to practice handwriting freely using a tablet. Unlike handwriting recognition systems [10][11][12][13][14], we perform a detailed matching between each input character with a known template character rather than a set of candidate characters. In our proposed system, we have two levels of matching: stroke-level and character-level matching. The stroke correspondence between the input character and the template is considered in both matching levels. The problematic strokes, which are suspected to contain stroke production errors, are identified. We have introduced the “miss-and-merge” stroke operation that can derive new character instances for both sample and template characters by either throwing away or merging problematic strokes. Each character instance contains information about a potential production error, and the overall production error is determined by the matching the character instances. The effectiveness and accuracy of our system are evaluated with challenging samples that contain multiple handwriting errors. This paper is organized as follows. The proposed method is described in section 1. The experiments, user studies and results are presented in section 2. Conclusions and future work are provided in section 3.

1. Proposed Method Our proposed system consists of two parts: the user interface and the Automatic Feedback and Analysis (AFA) tool. Fig. 1(a) shows the user interface for the learners to practice handwriting. The user has to follow the template character on the “Example Box” and then write on the “Exercise Box” using a tablet to provide the input character. By clicking the “Analysis” button, the AFA tool is invoked to analyze the input character. If there are any errors, feedback will be provided by highlighting the erroneous parts by red circles or shading the erroneous strokes with different colors. The flow of the proposed AFA algorithm is shown in Fig. 1(b). The online demo of our handwriting education system is available from the following link: http://vache.cs.cityu.edu.hk/ccls/.

K.-T. Tang and H. Leung / Teaching Chinese Handwriting by Automatic Feedback

Example Box

109

Exercise Box

Click “analysis” button for Automatic Feedback and Analysis (a) The user interface

(b) Flowchart of our proposed algorithm Fig. 1. The system description.

1.1 Data Collection The online handwriting data is collected by using a tablet. In our system, two kinds of characters will be considered: the template and sample characters. The template characters are skillful handwritings given by a teacher and they contain information about the correct stroke sequence and stroke production. The sample characters are student input handwritings that might contain various kinds of handwriting errors. 1.2 Stroke Correspondence As the first step of the proposed method, we find the stroke correspondence that represents a matching between the strokes in the sample and template characters. The matching cost between each stroke from the sample character and each stroke from the template character needs to be computed forming a cost matrix. The stroke correspondence can then be determined from the cost matrix. 1.2.1 Stroke matching costs The stroke matching cost is defined between a stroke in the sample character and a stroke in the template character. Both of these strokes are resampled to contain n data points. Assume that the data points of the sample stroke are denoted by ( xiS , yiS ) and the data points of the

template strokes are denoted by ( xiT , yiT ) , where i = 1,2,…,n. For the stroke matching cost, we consider the Euclidean distance (distance cost) and the direction difference (direction cost) between these data points. These two component costs are then combined to form the final stroke matching cost. (a) Distance Cost The distance cost is the average Euclidean distance between every data points on a sample-template stroke pair. There are two possible writing direction combinations for the stroke pair. The Euclidean distances for the stroke pair for these two cases are computed,

xiS  xiT  yiS  yiT and dic xiS  xnTi1  yiS  ynTi 1 . The distance cost i.e., d i CDist is defined as the minimum between these two values as shown in equation (1): 2

2

2

2

110

K.-T. Tang and H. Leung / Teaching Chinese Handwriting by Automatic Feedback

C Dist

1 n · §1 n min¨ ¦ d i , ¦ d ic ¸ ni1 ¹ ©n i 1

(1)

(b) Direction Cost The direction cost is the average sine values of the angle difference between every stroke segments on a sample-template stroke pair. The angle of a sample segment can be computed § yS  yS · as T iS arctan¨¨ iS1 iS ¸¸ and the angle of a template segment can be computed © xi 1  xi ¹ § yT  yT · arctan¨¨ iT1 Ti ¸¸ , where i = 1,2,…,n-1. Taking account of the two writing direction © xi 1  xi ¹ combinations, the direction cost CDir is defined in equation (2):

as T iT

C Dir

1 n 1 § 1 n 1 · min¨ sin T iS  T iT , sin T iS  T nTi ¸ ¦ ¦   n n 1 1 i 1 i 1 © ¹

(2)

(c) Combining the costs We combine the component costs CDist and CDir by a linear weighting method, and their weights wDist and wDir are determined based on the statistical analysis of the cost distributions for the matched and non-matched stroke pairs. Matched stroke pairs are the corresponding strokes between two handwriting samples of the same character and we obtain the handwriting samples by asking several users to write the same set of characters. Non-matched strokes pairs are those strokes that are not corresponding strokes between the two handwriting samples. By plotting the FMR (false matched rate) and FNMR (false non-matched rate) curves as shown in Fig. 2(a) and Fig. 2(b) from the cost distributions, we can determine how well each component cost can distinguish between matched and non-matched pairs through their equal error rates (EER) where FMR and FMNR converge to have minimum equal error. The lower the EER, the better is the component cost. The EER Dir EER Dist weights are computed as wDist and wDir EER Dist  EER Dir EER Dist  EER Dir respectively, and the EERs in the weights are used to boost the better component cost and suppress the poorer one. The combined cost is shown in equation (3): CCombined wDist C Dist  wDir C Dir (3)

Fig. 2. Error rates at different stroke matching costs.

1.2.2 Stroke Matching The stroke matching cost defined in the previous section is computed between each stroke in the sample character and each stroke in the template character. A cost matrix is then formed.

111

K.-T. Tang and H. Leung / Teaching Chinese Handwriting by Automatic Feedback

From the cost matrix, the stroke correspondence can be determined by minimizing the total cost of the matched stroke pairs using the Hungarian method [15]. 1.3 Problematic strokes Problematic strokes are strokes that are suspected to contain stroke production errors and they may appear in sample and/or template characters. Fig. 3 illustrates some samples that contain production error. When the numbers of stroke of sample and template characters are different, it is trivial that the additional strokes are problematic. Examples in these cases are shown in Fig. 3(a) and Fig. 3(b). On the other hand, it is more challenging to determine problematic strokes from matched strokes as shown in Fig. 3(c). It can be done by applying the result in Fig. 2(c) for classifying between matched and non-matched stroke pair. Non-matched strokes are likely to be problematic since those strokes are not similar in geometry as shown in Fig. 3(c), but they can still be paired up if no better pairs can be formed. Hence we define a threshold matching cost T that can clearly distinguish matched (normal) and non-matched (problematic) stroke pairs. If the threshold is defined to be too low, then many strokes may be wrongly labeled as problematic strokes. If the threshold is defined to be too high, then many problematic strokes may not be identified. From the result in Fig. 2(c), we choose the matching cost with EER as our threshold T (0.65 in our case). Both the template and sample strokes in any paired strokes with matching cost higher than T are problematic. Template Sample

1

2

3

--

A

B

C

D

Template Sample

1

2

3

A

B

--

Cost

0.4

0.5

0.3

Problematic?

No

No

No

Template Sample

1

2

3

A

B

--

--

Cost

0.4

0.5

Yes

Problematic?

No

No

--

Cost

0.4

0.8

--

Yes

Problematic?

No

Yes

Yes

Fig. 3. The problematic strokes are determined by stroke correspondence and stroke matching cost.

1.4 Character instance creation Some problematic strokes may contain production errors while others may not. There may be many problematic strokes and we have to investigate which problematic strokes in fact lead to stroke production error. To solve this problem, new character instances are derived from either the sample or template characters for representing a particular case of stroke production error. For creating the character instances, we introduce an operation called “Miss and merge”. “Miss” operation means taking away particular problematic strokes from the character, while “Merge” operation means merging two or more problematic strokes together. With these operations, two sets of character instances are formed: one set derived from the sample character and another set derived from the template character. The potential type of production error represented by a character instance can be inferred by the operation used to form that character instance. The explicit meaning of the character instances produced by each kind of stroke operations is summarized in Table 1. Derived character instances are given beside each operation applied on the problematic strokes (highlighted). The template strokes are labeled as 1, 2, 3, etc., while the sample strokes are labeled as A, B, C, etc.

112

K.-T. Tang and H. Leung / Teaching Chinese Handwriting by Automatic Feedback

Table 1. Different meanings of character instances of sample and template.

1.5 Character-level matching We perform a character-level matching that identifies the most similar sample-template character instance pair. The character matching cost is calculated from the average stroke matching cost of the matched strokes. The operations used to form the matched pair indicate the type of production error in the handwriting. We have introduced some measures to speed up our algorithm. The “Merge” operation is only applied to merge neighboring problematic strokes to form new character instances since the broken stroke errors often happen in a turning point of a stroke. In the character-level matching phase, only the character instances with the same number of strokes are matched thus filtering out many character instance combinations. The average time needed for creating a character instance and matching a pair of character instances are 0.5ms and 18ms respectively. On average, we need about 66ms to determine whether a character has stroke production errors. It shows our proposed method is quite efficient. 1.6 Automatic Feedback on handwriting errors

Fig. 4. Automatic feedbacks on handwriting errors.

After analyzing the input handwriting, a feedback will be given to the student to state whether his/her handwriting has handwriting errors and where the errors are. Fig. 4(a) illustrated a sample feedback when both stroke sequence error and stroke production error are committed by the student. In this case, the system first notifies the user what and where the production errors are. For production errors, missing and extra strokes are highlighted and broken or concatenated strokes are located by a small red circle. After the user has corrected the production error, the system can animate the stroke sequence in groups of correct sub-sequences by a minimum feedback method [5] as shown in Fig. 4(b).

K.-T. Tang and H. Leung / Teaching Chinese Handwriting by Automatic Feedback

113

2. Experiments and results We invited 65 public users to write some Chinese characters shown in Fig. 5(a) and a total of 920 handwriting characters are collected. One-tenth (92 samples) is used in training the stroke matching cost as mentioned in section 1.2.1. The rest (828 samples) is used as test data in our experiment. Fig. 5(b) shows the handwritings written by different people. Although variation is large, our system is still robust enough to check handwriting errors.

(a) Characters used in the dataset (b) Handwriting variations Fig. 5. Characters used in the dataset and sample handwritings.

Fig. 6 shows the types and counts of the handwriting errors in our testing data. To evaluate the performance of our system, we consider the Type I and Type II errors of each handwriting error. For example, if a sample character contains stroke production error only (Region 3 in Fig. 6) but the system wrongly identifies it as stroke sequence error (Region 2, not Region 3), it results in “Type I stroke production error” and “Type II stroke sequence error”. Experimental result in Table 2 shows that our proposed method can identify nearly all stroke sequence errors (0.74%). The error rates for identifying stroke production errors are also low. Relatively more correct samples (i.e. without error) are wrongly identified as stroke production error because some input strokes are badly written (e.g. too tilt) and the system often considers them as problematic strokes. Fig. 6. The types and counts of handwriting errors in our dataset.

Table 2. Performance evaluation of our method. Types of handwriting error Stroke sequence error Stroke production errors Stroke production errors with sequence error

Type I Type II Type I Type II

Error rate (%) of our Proposed Method

Type I

0.74% 0.58% 1.68% 2.62% 0.49%

Type II

0.48%

In addition, we have performed some user studies to evaluate the effectiveness of our proposed system for handwriting education. Sixteen university students are being invited as users and we randomly divide them into “Test” and “Control” groups. We have prepared an exercise with five Chinese characters “ᓳ”, “ᰗ”, “⬋”, “႑” and “㟙” which are often written incorrectly. The users are required to write each of these characters until the characters are written correctly. In the mean time, they can click the “Analysis” button for advice. The users in different group receive different kinds of feedback. In the “Test” group, the users can receive full feedback provided by our proposed method. In the control group, the users can just receive either “correct” or “wrong” with no extra guidelines.

Fig. 7. Learning performance evaluation.

The number of clicks of “Analysis” button and the time elapsed for all characters to be written correctly is recorded. Before the experiment, we record the time taken for normal writing and it is used for normalization to account for the writing speed difference among users. Fig. 7 shows the evaluation result. It is quite contrasting that the test group using our propose system can learn faster (shorter normalized exercise time) and they require less feedback from the system (fewer clicks).

114

K.-T. Tang and H. Leung / Teaching Chinese Handwriting by Automatic Feedback

3. Conclusion and Future work We proposed a new method that can check stroke sequence errors and stroke production errors simultaneously. In our proposed method, users can write freely without the writing sequence restriction as imposed by existing methods. Using the problematic strokes, we introduced miss-and-merge stroke operations to create character instances to identify potential production errors. Experimental results showed that our proposed method can check handwriting errors with an encouraging speed and accuracy. The learning performance of the students has been evaluated and it is found that they learn faster in Chinese handwriting using our system. As future work, we will study other kinds of handwriting errors such as the stroke reversal problem and develop methods to evaluate the beautifulness of the student handwritings automatically. Acknowledgments The work described in this paper was partially supported by a grant from City University of Hong Kong (Project No. 9360092). References [1] Takeshi Shimomura, “Informatics: input and output: Science of the stroke sequence of Kanji”, Proc. of the 8th Intl. Conf. on Computational Linguistics, pp. 270–273, 1980. [2] Nancy Law, W.W. Ki, A.L.S. Chung, P.Y. Ko and H.C. Lam. “Children’s stroke sequence errors in writing Chinese characters”, Reading and Writing: An Interdisciplinary Journal, Kluwer Academic Publishers, 10: 267-292, 1998. [3] Jianguo Li and Xiaozhen Zhang, “The design and implementation of multimedia intelligent tutoring system for Chinese characters”, IEEE Proc. of the 1st Intl. Conf. on Multi-Media Engineering Education, pp.459–463, July 1994. [4] Chun-Hung Tzeng, Leon Hsu, Chien-Ping Chen and C. Uema, “A multimedia project in teaching Chinese and Japanese at Ball State University”, IEEE Intl. Conf. on Multi Media Engineering Education, pp.445 – 452, July 1996. [5] Kerry Tsang and Howard Leung, “Teaching Stroke Order for Chinese Characters by Using Minimal Feedback”, Intl. Conf. on Web-based Learning (ICWL 2005), Hong Kong, August 2005. [6] Chwee Keng Tan, “An algorithm for online strokes verification of Chinese characters using discrete features”, 8th Intl. Workshop on Frontiers in Handwriting Recognition, pp.339 – 344, 2002. [7] C.L. Teo, E. Burdet, H.P. Lim, “A robotic teacher of Chinese handwriting”, HAPTICS 2002, pp.335 – 341, 2002. [8] Caroline Coit, “Peer review in an online college writing course”, IEEE Intl. Conf. on Advanced Learning Technologies, pp.902 - 903, 2004. [9] Carl Frélicot, Céline Rémi and Pierre Courtellemont, “School Level Recognition from Children's Drawings and Writings”, Intl. Conf. on Pattern Recognition 2002, pp.493-496, 2002. [10]Y. Tonouchi and A. Kawamura, “An On-Line Japanese Character Recognition Method Using Length-Based Stroke Correspondence Algorithm,” Proc. of the 4th Intl. Conf. on Analysis and Recognition, Vol.2, pp.633-636, 1997. [11]Y.T. Tsay and W.H. Tsai, “Attributed String Matching by Split-and-Merge for On-line Chinese Character Recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.5, No.2, pp.180-185, 1993. [12]W. H. Tsai and S. S. Yu, “Attributed string matching with merging for shape recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.7, pp.453-462, July 1985. [13]T. Wakahara, A. Suzuki, N. Nakajima, S. Miyahara and K. Odaka, “On-line cursive Kanji character recognition as stroke correspondence problem”, ICDAR 1995, pp.1059-1064, 1995. [14]Toru Wakahara and Kazumi Odaka, "On-Line Cursive Kanji Character Recognition Using Stroke-Based Affine Transformation", IEEE Trans. on Pattern Analysis And Machine Intelligence, vol. 19, no. 12, December 1997. [15]R.R. Burkard and E. Cela, “Linear Assignment Problems and Extensions”, P.M. Pardalos and D.-Z. Du, editors, Handbooks of Combinatorial Optimization, Dordreck: Kluwer Academic Publishers, pp. 75-149, 1999.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

115

Developing a Practical Domain Knowledge Base and Problem Solving System for Intelligent Educational System of High School Chemistry Nana Ishima, Toru Ueda, Tatsuhiro Konishi, Yukihiro Itoh Faculty of Information, Shizuoka University, Japan E-mail: {cs1055, gs06008}@s.inf.shizuoka.ac.jp {konishi, itoh}@inf.shizuoka.ac.jp Abstract: In this paper, we describe the development of our intelligent educational system for high school chemistry. Our goal is to put our system in practical use. To achieve our goal, the system must be usable for at least half a year or more. So we focus on the scale of a domain knowledge base (DKB) and a problem data base. We examine the framework of the chemical world model, and describe problem representation and knowledge representation. We tried to expand the DKB and the problem data base. We include an example of problem solving process using the DKB. Keywords: Intelligent Educational System, Domain knowledge representation, Problem solving, practical educational system

Introduction Intelligent Educational Systems (IES) have been developed in recent years; few of these are yet sufficiently developed for practical classroom use. Most of the proposed systems are prototypes that focus on specific techniques or theories. We aim to develop an IES supporting exercise for practical use. There are many Conventional Educational Systems (CES) supporting study in the educational field. For example, most Learning Management Systems (LMS) have a simple testing environment [1][2]. Teachers and learners can look over results item by item and apply it to the next learning. However, CES can only accumulate a learning history arranged according to problem. On the other hand, an IES can accumulate learning history, not only by problem, but also by knowledge, because the IES automatically understands the relations between the domain knowledge and problems by solving problems by itself. Therefore, if our system is introduced in the educational fields, teachers and learners can better diagnose issues. In the longer term the system is used, such advantage of IES becomes the more important. In our previous research, we have been developing the IES for high school chemistry [3][4]. In this paper, we reconstruct the IES supporting exercise in order to make our system more practical. To put the system in practical use, the system must be usable for long-term. In order to make it possible, we must finish the following tasks: expand a Domain Knowledge Base (DKB) and problem database; develop a problem solving system based on the DKB, an assessment system of learner’s answers, an explanation generating system, and a management system of learning history. In this paper, we discuss a framework of a chemical world model, and describe the problem representation and the knowledge representation. We also explain the problem solving process using the DKB.

116

N. Ishima et al. / Developing a Practical Domain Knowledge Base and Problem Solving System

1. Chemical problems the system deals with Based on discussions with high-school teachers, we determined that the minimum scale of DKB must cover all chapters of inorganic chemistry in the Japanese standard textbooks for first grade of high-school. Our current plan is to construct a DKB covering the following chapters: “Structures of materials and combination of particles”, “Variation of materials” and “Characters of materials (inorganic)”.Our system deals with the following five types of problems; they cover about 85% of problems in chapter 1 to 3 of standard workbook [6]. (1) Simulate a chemical phenomenon; a part of simulated result is the answer. Ex. What is the name of the salt formed, when neutralization of HCl and NaOH takes place? (2) Set up a material attribute value using numerical relation knowledge. Ex. Find the mol of 3.2g CH4? (3) Find the chemical equation’s coefficient. Ex. aH2 + bO2 ψ cH2O (4) Combine (1) and (2) or combine (1) and (3). Ex. How much caustic soda will be needed to neutralize 100ml of 2.0mol/l acetic acid? (5) The answer is an item in DKB. Ex. What is hydrogen’s RMA? 2. Knowledge Representation and Problem Representation At first, we describe the basic framework of the chemical world model. In general, we regard physical phenomena as continuous. However in high school chemistry, it is relatively rare to recognize chemical reactions as continuous phenomena, but we usually isolate discrete ones. Indeed, a chemical reaction formula represents only materials existing before a reaction and after it, without the details of changing state. As a result, we adopt the following framework to represent the chemical world model: the state of the world is represented by “static states”, “dynamic states” and their “causal relations”. The “static state” means a state in which all chemical materials are stable, such as before or after a chemical reaction. The “dynamic state” means a state in which some change is happening. The “causal relation” means a relation that triggers a change from a static state to a dynamic state or the reverse. We design our knowledge representation based on the framework. The DKB is composed of general knowledge of chemistry which is used when the system solves the five problem types. Table1 shows the classification of general knowledge of chemistry, and explanations of each knowledge. We tried to establish DKB on knowledge representation, but we found a technical problem. It is that a concept cannot be written uniquely, but sometimes variously. We propose a resolution for the problem using labels. Some attributes of a concept can have multiple values. Each value has a label indicating the situation in which the value should be selected. We explain the further details Fig 1: Representation of material conception knowledge , about the issue and resolution in [7]. attribute conception knowledge and numerical relation knowledge Next we discuss problem representation. As mentioned in section 1, our system deals with five problem types. Though problem solving algorithms are different in each type, a framework of problem representation is desirable to be coherent. Thus we defined a syntax of problem representationwhich does not depend on problem types. We call chemical problems composed by the syntax “query”. We explain the further details Fig 2: Representation of chemical phenomenon knowledge, about problem representation in [7]. Material conception know ledge

Numerical relation know ledge ID=6000

formula=>

ID=100

name=hydrogen molecule

dens ity=m ass /volum e

Chemical formula=H2

Attribute conception know ledge

MW =2

Density=ID3000

ID=3000

V olume=ID3001

name=Dens ity

Mass=ID3002

Numerical relation=ID6000

dominant conception =m olecule

Chemical phenomenon kn ow ledge

Chemical state know ledge ID=30000

ID=10000

name=neutralization

Contents =ID101(acid) ,ID102(bas e)

condition=ID30000

Process=ID30000,ID30001 ID30002

Chemical state know ledge

Numerical relation=ID4000

ID=30001

Neutralize(ID101,ID102)

Numerical relation know ledge ID=4000

Generate(ID103(water)

formula=acid_M㬍acid_Volum e 㬍acid_Valency=base_M㬍bas e_ Volum e㬍bas e_Valency

Cons ume(ID101,ID102)

ID104(salt))

chemical state knowledge and numerical relation knowledge

N. Ishima et al. / Developing a Practical Domain Knowledge Base and Problem Solving System

117

Table1. Knowledge representation [Knowledge on material] ˉMaterial conception knowledge (represents individual attribute information of a material and conception class to which the material belongs˅ ˉ name slot [The name of the material ˉ attribute slot [*1] ˉ dominant conception slot [An ID of material class to which the material belongs (superclass)] ˉ lower conception slot [An ID of subclass of the material] ˉ construction slot[Constituent particles and the construction of a material] ˉAttribute conception knowledge (has pointers to numerical relation knowledge which include the attribute’s definitional equation.) ˉ name slot [The name of attribute] ˉ numerical relation slot [IDs of numerical relation knowledge representing relative equations with the attribute] ˉ definitional equation [A definitional equation (For example, a density is defined as mass per unit volume, and the formula is “density= mass/volume“.)] [Knowledge on phenomenon] ˉChemical phenomenon knowledge (has causal connection between a condition in which the dynamic phenomenon occurs and process of the dynamic phenomenon, and works as a production rule; initial condition slot is the condition part, and process slot is the action part of the rule.) ˉname slot [The name of phenomenon] ˉinitial condition slot [An ID of chemical state knowledge] ˉprocess slot [IDs of chemical state knowledge of initial state and of changing state] ˉnumerical relation [An ID of numerical relation knowledge which is applicable under the dynamic phenomenon] ˉChemical state knowledge (represents a state in the chemical world model. It indicates existing materials, changes in the chemical world model.) [Numerical relation knowledge] ˉType1. Applicable under a particular phenomenon ˉType2. Applicable for an attribute of a particular material class ˉType3. Applicable for an attribute of any material class (Each type of numerical relation knowledge is linked from chemical phenomenon knowledge, material conception knowledge, and attribute conception knowledge respectively.) *1 Attributes slot is classified into two types. One is that the attribute value is decided uniquely when deciding material name and attribute name (ex. RMA, atomic number).In this case, the attribute value is written in the slot.The other is that the attribute value is not decided uniquely when deciding material name and attribute name (ex. volume, mole, mass).In this case, an ID number of attribute conception knowledge is written.

3. Problem Solving System (PSS) The PSS has the following five units: Control routine, Simulator, Query processing routine, Relation determination routine, and Equation routine. The system has also DKB [Fig 3]. We explain the details of PSS behavior in [7].The problems of type 3 in Table 1 can be solved in the following way(Fig.4) : (1)The system checks information in the problem representation called “query” (Volume of CH4 is asked, and weight of CH4 is 3.2g). (2)The system searches a useful equation to specify asked value, and sets terms in the equation as subgoals (The equation 1 may be useful to specify the volume. Substance quantity and Mole volume become a subgoal, but the latter is achieved immediately because the value is found in the DKB). (3)The system tries to solve all the subgoals in the same way as (2) recursively (Equation 2(Fig.4) may be useful to specify the Substance quantity. Weight and MW become subgoals and achieved soon, as both of them are found in the DKB or the query). (4)If all of the subgoals are achieved, the system calculates the asked value by the equations (the system specifies the substance quantity of CH4 by Equation 2, and the volume of CH4 by Equation 1). Type 3 problems include Type 1 and Type 2. As for Type 4, we must add a routine for

118

N. Ishima et al. / Developing a Practical Domain Knowledge Base and Problem Solving System

solving for an undetermined coefficient to Equation routine. As for Type 5, the system does not have to infer because answers are written in DKB directly. The system records problem solving process including used knowledge and the order of applying the knowledge. We can use this record for making detailed learning history. And we also design Explanatory Text Generator which converts problem solving process to explanation.

Fig 3: Overview of Problem Solving System

4. Implementation We have implemented DKB using XML. We analyzed a textbook [5] and workbook [6], and counted the items of knowledge. The number of Material conception knowledge is 176, the number of Attribute concepts is 30, the number of Chemical phenomena is 100, the number of Chemical states is 160, and the number of Numerical relations is 72. We have already implemented half of them. We also have Fig 4: An example of solving a problem by our system implemented most of PSS and designing Simulator using Java (Fig 4). Now our system can solve 70 problems of Type 2, 4, 5. 5. Conclusion We have been developing the IES which can be used long-term, focusing on practical use. If our system is completed, we think the system can deal with learning chemistry from half to one year. This is conducive to practical use of IES. We prepare a DKB and the basic problem data base of a comparable scale with a standard workbook in advance. However, teachers in high school would want to edit problem data based on their experiences. In order to satisfy their requests, we will have to design authoring tools which help teachers edit problem data base by themselves in the future. As the future tasks of this study, we will finish developing PSS and also developing an interactive system which dialogues with learners on a problem solving process. References [1] http://moodle.org/ [2] http://www.xu.edu/its/elearning/blackboard.cfm [3] I.Takahashi, T.Konishi, Y.Itoh:"On a Framework of an Educational System which Acts both as ITS and Micro-World", Proc. of Int̉l Conf. On Comp. in Edu. ,Vol.1,pp.856-859. (1999) . [4] T.Konishi and Y.Itoh : "Domain World Models Represented from Variable Viewpoints for ICAI Systems of High-school Chemistry", Lecture Notes in Artificial Intelligence 1114 (PRICAI'96: Topics in Artificial Intelligence), pp.495-509, Springer-Verlag (1996). [5]“Koutou gakkou KagakuΣ”, Suuken Syuppan (2002) (in Japanese) [6] “Kihon mondaishu Kagaku IB”, Shinkou shuppansha Keirinkan (in Japanese) [7] N.Ishima, T.Ueda, T.Konishi, Y.Itoh: ”Developing Problem Representation Knowledge Representation and Problem Solving System for Intelligent Educational System of High School Chemistry” , The workshop 5 at ICCE 2006: Problem-Authoring, -Generation and -Posing in a Computer-Based Learning Environment, 2006(to appear).

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

119

The COLAC Model: Collaborative PaperWriting in the Humanities Guillaume Schiltz, Andreas Langlotz Department of English, University of Basel, Switzerland [email protected] Abstract: In this paper we introduce a pedagogical model that trains writing skills in a blended learning environment (online/in-class). The model has been successfully implemented in several university courses. The results of a formative evaluation support the effectiveness of the model but also point to problems related to the unfamiliar collaborative learning methods. Keywords: pedagogical models, collaborative writing

Introduction At the moment all European universities are going through a fundamental change of their curricular system; this is probably the most far-reaching structural reform in recent times. Termed as the Bologna Process [1], the reform mainly aims to bring degrees into line with each other and to make higher education more transparent as well as uniform. Besides the changes at the structural level, the Bologna Process also aims at pedagogical changes which include a clear shift from teacher-centered to learner-centered methods and the promotion of essential soft skills, such as social and media competence. These secondary aims support the integration of e-learning scenarios, even in fields and subjects that have so far remained fairly reluctant to technical innovation, such as the humanities. Writing academic research papers, i.e. articulating personal thoughts and arguing according to academic principles is one of the most important operational skills promoted in the humanities. Traditionally, the process of ‘how to write an academic paper’ has mainly been imparted by providing students with the necessary tools (reference lists, research inquiries) and by teaching the formal layout of a paper (structure, style sheet). However, writing itself should be seen as a joint communicative process: thoughts and ideas have to be transmitted from the author to the reader. This transmission is subject to principles of audience design and has to follow the basic rules of the genre ‘academic paper’. At university level these communicative aspects of paper writing often are only conveyed implicitly in the coaching between the student and the instructor. Therefore, the emergent ability of writing academic papers mostly results from the continuous practical experience of individual students and the corresponding individual communication with the instructors. In the past a great number of studies have focused on the cognitive and practical principles that guide students in writing papers (e.g. [2]). The results of these studies were even transferred to technical environments [3] to support the learning of writing. A pedagogical model, however, that teaches the more communicative aspects of academic paper writing in a general way and that is easy to implement has been missing so far.

120

G. Schiltz and A. Langlotz / The COLAC Model: Collaborative Paper-Writing in the Humanities

The COLAC model (Colac referring to a sleepy town in Australia) introduced here aims to link traditional classroom-teaching methods with the advantages of computermediated communication. Thus, it is an attempt to implement the basic pedagogical goals of the Bologna Process. The model is based on the following pedagogical approaches: x It is designed as a blended learning component in a location-based teaching environment according to Kerres’ 3C-model [4]. x It implements the three major elements of cooperative learning: social interdependence, positive relationship and promotive interaction [5]. x It relies on the activity theory within communities of practice [6].

1. The COLAC Model The basic setting of the COLAC model (fig. 1) simulates the process of an academic symposium, i.e. sharing knowledge among a peer community, discussing one’s research results and thus building up a reputation of expertise. Often, experts work together in teams and thus the model also relies on group work.

Figure 1: the COLAC model At the beginning, groups have to be formed with every participant being the member of one group. Each group has to choose one topic from a list of basic research questions. In the model each group performs two distinct roles. First, it acts as an author group by working out a paper on the chosen topic. Second, it has to review the paper of another group and present this paper in a simulated conference in the classroom. The peer-review is the fundamental part of the model and consists of a thorough discussion between the author group and the review group. Based on the outcome of this discussion the author group has to refine its paper continually and must eradicate all

G. Schiltz and A. Langlotz / The COLAC Model: Collaborative Paper-Writing in the Humanities

121

uncertainties, flaws, and weaknesses. Both writing and reviewing are realized online, preferably with the support of asynchronous communication technology. After the review is finished, the revised paper is made available to all course participants. The students should have enough time to read the paper thoroughly and to prepare questions for the presentation. The in-class paper presentation is split between a short oral summary of the paper and a subsequent discussion of the paper topic. During the presentation the authors are not involved but take notice about how their paper is received. At the end the review group is assessed depending on the expertise its members manifested during the presentation. The instructor also adopts a dual role in the overall model. On the one hand, he/she has to provide the organizational framework (time management, course structure) in a clear and comprehensive way. On the other hand the instructor has to act as an expert and as a coach by supplying help whenever specialist or social support is needed. Fig. 1 depicts the model in detail. It is divided into two distinct phases (paper writing, paper presentation), including four action groups (author group, review group, course participants and instructor), each one performing different actions (write, revise, summarize, discuss, read, ask, assess). Although the formal goal of the model is defined by writing and presenting an academic research paper, the actions of each group involved include a number of secondary learning aims such as teamwork organization, discursive argumentation and conflict resolution. Computer-mediated collaboration is essential for all actions during the paper writing phase, as well as within the action groups (authors have to elaborate the paper, reviewers have to organize the review), as between the action groups (authors and reviewers have to discuss the paper). Furthermore, the computer is used as a central repository for the paper and enables collaborative writing and revising facilities through wiki or similar technologies.

2. Actual Implementation and Outlook A pilot implementation with 44 undergraduate students was successfully conducted as part of an e-learning course in 2004 [7]. Since then we have been improving the model and transferred it to a Moodle platform. Now in 2006 we are running the model again with a total of 68 students in a joint course between two universities. Some of the action groups being geographically remote, the presentation phase this time will be partly realized as a joint video conference. Since 2006 the model is part of the national e-learning initiative Swiss Virtual Campus (http://www.virtualcampus.ch/) and it is planned that starting from 2007 it will be implemented simultaneously at three different universities in Switzerland.

3. Evaluation Results As a result of the formative evaluation carried out after the course, the great majority of students claimed that they had achieved the overall course aims (fig. 2). This self-appraisal can be supported by the instructor’s assessment (quality of the papers, active participation, final test). When asked for more differentiation, again the great majority of students answered that they judged the paper review as informative (‘I learned a lot’). The results for motivation (‘I liked it’) and for appropriateness (‘It should be adopted by other courses’), however, significantly tend to a less positive judging. Informal interviews gave evidence that the main cause of this divergence is due to the fact that all students were unfamiliar with collaborative learning. While working in the model they had to revise and

122

G. Schiltz and A. Langlotz / The COLAC Model: Collaborative Paper-Writing in the Humanities

adapt their learning strategies, which caused them to feel insecure and hesitant about the benefits of such an approach. In the meantime, however, we are observing that collaborative learning skills are becoming more developed among students. acquisition of course aims (student's judgment)

teamwork competence

1

e-learning skills

factual knowledge

20%

11

30

2

25

3

14

25

0%

20%

1

2

15

40%

60%

80%

100%

students poor

average

good

excellent

Figure 2: student’s judgment on the course aims rating of the paper review

inform.

motivat.

approp.

50%

6

19

19

17

25%

11

26

19

0%

25%

5

7

50%

75%

100%

students negative

average

positive

Figure 3: student’s rating of the paper review

References [1] Reichert, S., and Tauch, C. (2005) Trends IV: European Universities Implementing Bologna. EUA Report, Brussels. (http://www.eua.be/eua/jsp/en/upload/TrendsIV_FINAL.1117012084971.pdf) [last visited on May 20th, 2006]. [2] Flower, L. (1988) The Construction of Purpose in Writing and Reading. College English, 50, 5, 528550. [3] Neuwirth, C.M., and Wojahn, P. (1996) Learning to write: Computer support for a cooperative process. In: Koschmann, T. (Ed.) CSCL: Theory and practice of an emerging paradigm. Lawrence Erlbaum, Mahwah, NJ, 147–170. [4] Kerres, M., and de Witt, C. (2003) A didactical framework for the design of blended learning arrangements. Journal of Educational Media, 28, 101-114. [5] Johnson, D.W., Johnson, R., and Holubec, E. (2002) Circles Of Learning: Cooperation in the classroom, (5th Edition). Interaction Book Company, Edina, MN. [6] Hewitt, J. (2004) An exploration of community in a Knowledge Forum classroom: An activity system analysis. In: Barab, S., Kling, R., and Gray, J. (Eds.) Designing virtual communities in the service of learning. Cambridge University Press, Cambridge, MA, 210-238. [7] Schiltz, G., and Langlotz, A. (2004) eHistling - Integrating Communication and Cooperation in the Humanities. In: McKay, E. (Ed.) International Conference on Computers in Education 2004. Book of Abstracts. Common Ground, Altona, VIC, 149 (full presentation on CD-ROM).

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

123

Experimental Investigation and Implementation of Support for Problem Generation by Presenting Cases Kazuaki Kojima and Kazuhisa Miwa Graduate School of Information Science, Nagoya University, Japan [email protected]

Abstract: The usefulness of cases in creative generation has been widely discussed and well recognized. However, it has been also experimentally indicated that introducing cases can limit human creative generation. In this study, we investigate the effects of case presentation in the task domain of generating mathematical word problems. Mathematical word problems have two essential attributes: surface problem situations and the mathematical structures of solutions, the control of which is recognized as an important issue in learning mathematics. In response, we present problems as cases to participants while controlling those attributes, with the results indicating that such presentation actually provides different constraining effects based on the control to their problem generation. Based on the results, we propose a support system for generating word problems, which presents problems as cases by controlling similarities. Keywords: Creative generation, case presentation, word problems, problem generation

Introduction People often utilize past experiences or existing examples as cases in creative generation. It has been well explored that cases are useful during many processes of creative generation (e.g., see [1]). The presentation of cases to support the creative generation has been proposed and implemented in a variety of tasks. However, introducing cases can also limit human creative generation. It has been experimentally indicated that human creative generation tends to conform to examples given prior to starting the generation tasks [7]. Such a constraining effect by examples is somewhat undesirable, because the essential factor in creative generation is the production of novel output. On the other hand, providing appropriate constraints on creative generation is important to support concept formation and enhance creativity [4,5]; therefore is not necessarily wise to remove all constraints. Therefore, it is necessary to investigate the relation between cases and their constraining effects in creative generation in order to implement creative generation support by presenting cases. In this study, we experimentally investigate the effects of case presentation in the task domain of generating mathematical word problems. Then we implement a computer-based system that supports problem generation by humans by presenting problems as cases. Mathematical word problems have two attributes that are essential in problem solving; surface problem situations denoting contextual settings expressed in texts such as "purchase of goods," and the mathematical structures of solutions. In the context of mathematics education, it has been recognized as an important issue to present students with various problems while controlling the similarities [2]. Moreover, in the context of problem-posing education, it has been pointed out as important but also difficult to generate a broader range of problems from

124

K. Kojima and K. Miwa / Experimental Investigation and Implementation of Support

formal symbolism and informal situations [3]. According to the background, it is also beneficial in the learning of mathematics to investigate and implement support for creative generation by presenting problems as cases.

1. Experimental Investigation In our investigation, participants were asked to generate mathematical word problems. In the task of problem generation, they were presented problems as cases that were produced by controlling similarities in problem situations and solutions. We verified the effects of case presentation based on the relation between participants' problems and the presented cases.

1.1 Method One hundred and forty-five undergraduates from a class on psychology for design participated in our investigation. The experimental procedures were as follows. 1.

2. 3.

Generating a problem (first) Every participant was presented with an example of a word problem and asked to generate a new problem from the example. Presenting cases The participants were presented with three problems as cases. Generating a problem (second) Every participant was asked to generate another problem from the example. In the second round of problem generation, the participants could refer to the problems presented in Procedure 2.

Each participant was randomly assigned to one of six experimental groups. Every group was given a different set of problems as cases in Procedure 2. The groups and the set of problems for each group were as follows. II1: problems whose situations and solutions were identical to the example II2: problems whose situations and solutions were identical to the example, but some of parameters in the texts were different from the example problem DI: problems whose solutions were identical to and whose situations were different from the example ID: problems whose situations were identical to and whose solutions were different from the example DD: problems whose situations and solutions were different from the example Control Group: geometry problems that had no attributes similar to the example identical

Solutions

different

Ex Problem situations

A

C

B

D

different

Figure 1. Categories for evaluating the participants' problems

Problems generated by the participants were evaluated according to categories. The categories were specified based on similarities in problem situations and solutions between the example problem and the problems generated by the participants. The categories were as follows. A: problems whose situations and solutions were identical to the example B: problems whose solutions were identical to and whose situations were different from the example

K. Kojima and K. Miwa / Experimental Investigation and Implementation of Support

125

C: problems whose situations were identical to and whose solutions were different from the example D: problems whose situations and solutions were different from the example

Figure 1 shows the categories for evaluating the participants' problems.

1.2 Results In the first round of problem generation, the proportions of participants who generated problems of category-A, B, C, and D were 57.1%, 21.4%, 16.1%, and 5.4%. Thus, these results indicate that almost all participants generated problems whose situations and solutions were identical to the example problem when they had not seen any cases.

Figure 2. Proportions of participants who generated problems in each category (second round)

Figure 2 shows the proportions of participants who generated problems in each category in the second round of problem generation where the participants had already examined cases. Similar to the first round, more than half generated category-A problems in Groups II1, II2 and the Control Group in the second round. On the other hand, the proportion of category-B problems increased in Group DI, that of C in Group DI, and that of D in Group DD in the second round. These results indicate that case presentation influenced problem generation, and that the presentation of different cases had different effects on problem generation.

1.3 Discussion The results described above confirmed that presentation of different cases had different effects on problem generation. In Groups DI, ID, and DD, the similarities between the example problem and problems, increasing in the second round of generation, were consistent with those between the example problem and the presented cases in each group. Thus, case presentation might have had an effect that allowed the participants to learn the relation between the example problem and the cases, even though they were not told to do so. According to the results of our investigation, we propose a support system for problem generation that presents cases by controlling similarities. Since the aim of our system is to assist users in generating a broader range of problems, we have implemented functions in our system to evaluate the users’ problems and to present various cases while controlling similarities based on the evaluation.

2. Implementation of the Problem Generation Support System In this study, we propose a system that supports users in generating word problems, which presents problems as cases by controlling similarities in problem situations and solutions. In this study, our focus is on the aspect of problem generation as a creative generation task. Our system gives users a task to generate new problems from an example problem, and assists them

126

K. Kojima and K. Miwa / Experimental Investigation and Implementation of Support

in appropriately generating various problems. Our system supports the users by evaluating their problems based on similarities in two attributes of problem situations and solutions, and by presenting problems as cases that have various features in these two attributes. Since our system requires a variety of problems to function properly, it is assumed that our system stores problems propagated in advance by the problem-generation system [6]. In our system, a user is first given an example problem and prompted to generate a new from it. The user generates and inputs the problem into the system. In this phase, the system in turn requires (1) objects to appear in the problem text (such as apples or pencils), (2) numeric values to be included in the text for solving the problem, (3) equations for solving the problem, and (4) the problem text itself. The system analyzes the numeric values and the equations to check whether the solution can be solved, thus preventing any problems that have inappropriate solutions from being accepted. The system then estimates the problem situations. It uses situation-estimating models, each of which is constructed from nouns and verbs in the texts of problems in the casebase comprising identical situation. The system then indicates evaluation of the user's problem, which is expressed by showing similarities between the example problem and his/her problem, using the same representation as in Fig. 1. Simultaneously, it retrieves and presents some problems as cases. Our system can present various cases by controlling the similarities in problem situations and solutions, such as presenting problems whose solutions are identical to and whose situations are different from the user's problem. As described above, our system repeatedly evaluates problems devised by users and presents cases based on the evaluations. The intention is to provide various constraints to the users’ problem generation. We believe that the constraints allow the users to generate a broader range of problems.

3. Conclusions In this study, we experimentally investigated the effects of case presentation in the task of generating mathematical word problems. According to the results, we proposed a support system for generating word problems, which presents cases to users by controlling similarities.

References [1] Domeshek, E.A., Kolodner, J.L., and Zimring, C. (1994) The Design of a Tool Kit for Case-Based Design Aids, In Proceedings of International Conference on Artificial Intelligence in Design, pp. 109-126. [2] English, L.D. (1997) Children's reasoning processes in classifying and solving computational word problems. Mathematical reasoning: Analogies, metaphors, and images, pp. 191-220, Mahwah, NJ: Lawrence Erlbaum Associates. [3] English, L.D. (1998) Children's Problem Posing within Formal and Informal Contexts, Journal for Research in Mathematics Education, vol. 29, no. 1 pp. 83-106. [4] Finke, R.A., Ward, T.B., and Smith, S.M. (1992) Creative cognition: theory, research, and applications, MIT Press. [5] Hori, K. (1994) A Hypothesis for Discussing the Effect of the Systems for Aiding Creative Concept Formation, Transactions of Information Processing Society of Japan, vol. 35, no. 10, pp. 1998-2008. [6] Kojima, K., and Miwa, K. (2006) Evaluation of a System that Generates Word Problems through Interactions with a User, Lecture Notes in Computer Science, Springer-Verlag, vol.4053, pp.124-133 (2006). [7] Smith, S.M., Ward, T.B., and Schumacher, J.S. (1993) Constraining Effects of Examples in a Creative Generation Task, Memory & Cognition, vol. 21, no. 6, pp. 837-845.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

127

A Computer-Based Environment for Learning by Problem-Posing as Sentence-Integration Tsukasa Hirashimaa, Takuro Yokoyamab, Masahiko Okamotoc, Akira Takeuchib a Hiroshima University, Japan b Kyushu Institute of Technology, Japan c Osaka Prefecture University, Japan [email protected] Abstract: Learning by problem-posing is a well recognized as an important way to learn mathematics. In this paper, we describe a computer-based learning environment for learning by problem-posing as sentence-integration. The learning environment was practically used in arithmetic classes of the second and third grade students in several elementary schools. The definition of “learning by problem-posing as sentence-integration” and the framework of the learning environment are described in this paper. Keywords: Problem-Posing, Interactive Learning Environment, Sentence-Integration

Introduction Learning by problem-posing is a well recognized as an important way to learn mathematics [1,2]. We have investigated computer-based learning environment for interactive problem-posing that is composed of (1) problem-posing interface, (2) problem diagnosis function, and (3) supportive feedback for correcting or completing the posed problems, and confirmed that learning with these environments is useful to improve student’s problem-solving abilities [3-5]. However, the problem-posing is often difficult for lower grade students. In this paper, we described a learning environment for learning by problem-posing as sentence-integration, in order to keep the worth of problem-posing as a learning method and also to simplify the task of problem-posing. In the problem-posing as sentence integration, several simple sentences are provided to the students. The students, then, select necessary sentences and arrange them in an appropriate order. Although the process to integrate simple sentences remains as the characteristics of problem-posing, the process to make simple sentences is simplified to the process to understand them. Therefore, the "problem-posing as sentence integration" is a promising approach to satisfy both (I) simplification of problem-posing task for lower grade students in elementary schools, and (II) keeping the worth of problem-posing as a learning method. In Section 1, learning by problem-posing is discussed in more details. A learning environment for problem-posing as sentence-integration is described in Section 2.

1. Learning by Problem-Posing 1.1 Categorization of Problem-Posing In our research of "Learning by Problem-Posing", the "problem" is defined as follows. Problem = “Given-Information” + “Required-Information”

128

T. Hirashima et al. / A Computer-Based Environment for Learning by Problem-Posing

"Problem-Solving", is an activity to derive required-information from the given-information and then the problem solving should be able to complete deductively. "Solution Method" is, then, the method that is used in the problem solving. For example, when an arithmetical word problem is composed of the following three sentences {(I) Tom had five pencils. (II) Ken received three pencils from Tom. (III) How many pencils does Tom have?}, Sentence-I and –II are the given-information and Sentence-III is the required information. The solution method, then, is “5-3”. These definitions cover most of the problems used in problem exercises in arithmetic, mathematics, physics, and so on. Based on this definition, problem-posing can be completed by adequately combining the following three elements: (1) given-information, (2) required-information, and (3) solution method. These elements, then, are used as constraints that a student has to satisfy in problem-posing. In this subsection, forms of problem-posing are categorized from the viewpoint of the three elements. The following three forms are popular as problem-posing forms, namely, (A) to pose a problem that can be solved by a specific solution method, (B) to pose a problem by modifying a specific problem, (C) to pose a problem based on a specific story or picture. In Form-A, a solution method is used as constraint of problem-posing. We call this problem-posing form as "solution-based problem posing". In Form-B, given-information and required-information are used as constraint of problem-posing this form is called as "problem-based problem-posing". In Form-C, given-information, that is, story, is used as constraint of problem-posing; this form is called as "story-based problem-posing". Problem-posing that dealt with in our research is solution-based problem-posing. In the next subsection, several ways to realize solution-based problem-posing are described. 1.2 Solution-Based Problem-Posing In solution-based problem-posing, a learner is required to make a problem and the problem should be composed of given-information and required-information that can be solved by a specified solution method. To realize this exercise, it is important to provide the way to let a learner to make the composition of given-information and required-information. In a normal classroom, problems are posed by natural language, but such problems written by students often include errors as language. Because to deal with such erroneous sentences is difficult with current natural language processing techniques, to diagnose a problem composed of such sentences is not realistic. In the learning of arithmetical word problem, it is assumed that a learner has already had the ability to understand the meaning of each sentence in a problem and it is expected that the learner acquires the ability to understand the problem mathematically. In learning from problem-posing, therefore, it isn't indispensable to pose a problem by using natural language written by a learner. Based on these considerations, we have proposed three types of problem-posing methods as follows: (A) sentence template method [3], (B) problem template method [4, 5], and (C) sentence card method. In this subsection, because of page limitation, the outline of only the sentence card method has been explained. In the sentence card method, a learner is provided with several cards with a sentence in each card. The learner, then, is required to select some of them and to sort it out in a proper order. Although the process to make a sentence by combining concepts in other methods is substituted for the process to understand the sentences on the cards, the process to integrate simple sentences is same with the other problem posing methods. Therefore, the template card method is an approach to realize (1) to keep the worth of problem-posing as a learning method and (2) to simplify the task of problem-posing. A typical process model of problem-solving of word problems [6] is shown in Figure 1. The processes of transformation and integration are characteristics process of

T. Hirashima et al. / A Computer-Based Environment for Learning by Problem-Posing

129

word problem. In the transformation process, natural language is interpreted linguistically. A linguistic interpretation of a natural language sentence is corresponds to “sentence” in Figure 1. In the integration process, they are integrated mathematically. The structure made by the integration process is often called “problem structure” and it is describes the understanding of the problem. Adequate solution method is selected by using the structure. From the viewpoint of learning of mathematics, the integration process is far more important than the transformation process in the model. In the problem-posing by the sentence template method and the problem template method, transformation and integration are intermingled. Because the problem-posing with sentence card method is the problem-posing focused on the integration process, we call this type as "problem-posing as sentence integration". In the next section, a learning environment for interactive problem-posing as sentence integration, named MONSAKUN, is described.

Figure 1. A Process Model of Problem-Solving of Arithmetical Word Problems.

2. Learning Environment for Problem-Posing: MONSAKUN 2.1 Interface for Problem-Posing The interface of problem-posing in MONSAKUN is shown in Figure 2. The area in left side, imaged blackboard, is "problem-composition area". At the top of the area, a calculation expression is presented. A learner is required to pose a problem that is able to solve by the calculation expression. Here, the expression is the solution-method. Although the expression itself is easy, the learner has to consider the combination of not only a number but also a subject, object and predicate in each sentence. The three blanks in the area are the ones to put sentence cards. Sentence cards are presented at right side of the interface. A learner can move the card by drag&drop method freely in the interface. When a learner pushes "diagnosis button" under the problem-composition area, the system diagnoses the combination of sentences. The results of the diagnosis and message to help the learner's problem-posing is presented by Figure 2. Problem-Posing Interface of MONSAKUN. another window. 2.2 Diagnosis of a Posed Problem A problem posed by a learner is diagnosed by the following three viewpoints: (1) problem

130

T. Hirashima et al. / A Computer-Based Environment for Learning by Problem-Posing

type, (2) relations of concepts, and (3) numerical relation. The problem type is diagnosed by the combination of sentence type. For example, in the change problem, the first sentence has to describe a number of something exists, and the second sentence has to describe a change of the number. By this diagnosis, the type of problem the learner tried to pose can be detected. If the type can not be detected, that is, the combination of sentences is wrong, the system gives advises that what kind of sentence should be put in each blank. When the problem type is detected, the relations of the concepts in the sentences can be diagnosed. Depending on each problem type, the conditions that the relations have to satisfy are different. For example, in the combination problem, the numbers should belong to the concepts that are able to add mutually, like the number of apples and the number of oranges. In the change problems, the object of the change in the second sentence should be appeared in the first and the third sentence. By using such conditions, the relations of concepts are diagnosed. When the problem satisfies the conditions, a calculation expression can be made from the problem. If the problem doesn't satisfy a condition of the problem type, the system judge the problem is wrong. Then, the system gives advises that what kind of condition should be specified. In the diagnosis of numerical relation, a calculation expression is derived from the problem and then compared with the one that is the target of the problem posing. When the two expressions are the same, the posed problem is correct. If the derived expression is different only in the number, the system indicates the difference. When the expression is different in the operation, the system indicates that the posed problem is solved by the difference operation. When the calculation expression derives a minus number, the system indicates that the calculation is impossible because minus number has not been taught in elementary school. Since this case is happened only in subtraction, the system indicates that "it is impossible to subtract this number from that number".

3. Conclusion Remarks The learning environment was practically used in arithmetic classes of the second and third grade students in several elementary schools. As the results, we confirmed that teachers and students accepted it as a useful tool for learning. Besides, analysis of scores of the pretest and posttest as a function of condition and group suggested that the interactive learning environment could certainly improve the children’s problem solving performance. The details of the practical use and the analysis was omitted in this paper, they will be reported in another paper.

References [1] Silver, E.A., CAI, J.: An Analysis of Arithmetic Problem Posing by Middle School Students, Journal for Research in Mathematics Education, Vol.27, No.5, pp.521-539, 1996. [2] English, L.D.: Children’s Problem Posing Within Formal and Informal Contexts, Journal for Research in Mathematics Education, Vol.29, No.1, pp.83-106, 1998. [3] A.Nakano, T.Hirashima, A.Takeuchi: Problem-Making Practice to Master Solution-Methods in Intelligent Learning Environment, Proc. of ICCE'99, pp.891-898(1999) [4] A. Nakano, T. Hirashima, A. Takeuchi: A Learning Environment for Problem Posing, Proc. of ICCE2000, pp. 91-99 [5] A. Nakano, T. Hirashima, A. Takeuchi: An Evaluation of Intelligent Learning Environment for Problem Posing, Proc. of ITS2002, pp.861-872ʳ (2002). [6] Kintsch, W., Greeno, J.G.: Understanding and Solving Word Arithmetic Problem. Psychological Review, 92-1:109-129ʳ (1985)

Discussion

This page intentionally left blank

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

133

The Impact of Structured Discussion on Students’ Attitudes and Dispositions toward Argumentation Khai Seng HONG, Ole C BRUDVIK, Yam San CHEE National Institute of Education, Nanyang Technological University, Singapore [email protected] Abstract: Argumentation skills are highly valued in both education and business. As a process, participating in argumentation helps a person to develop their meta-cognitive and higher-order thinking abilities. This paper reports on empirical results on middle-school students’ changes in attitudes towards argumentation as part of an ongoing design-based research study. Past attempts by researchers to foster students’ argumentation skills have met with mixed results. General Web-based discussion boards often do not provide the structures and process scaffolds to help students acquire the target skill. In this study, a web-based structured argumentation board with sentence openers as scaffolds was designed to support students’ engagement with argumentation over a four week long intervention. Two questionnaires, a pre-post and a post-then-pre, were designed to measure students’ attitudes towards argumentation. The two questionnaires were used to identify any treatment dependent “response-shift bias”. Statistical results showed an improvement in students’ attitudes toward argumentation. Qualitative analysis of student essays was also carried out and will be reported separately. Keywords: argumentation, CSCA, student attitudes, critical thinking

1. Introduction Argumentation is a higher-order thinking ability highly valued in both education and business problem solving circles. However, the quality of people’s argumentation skills, in general, has been found to be low. Kuhn’s [1] study of argument-based reasoning found that, in everyday reasoning, people readily make assertions about the causes of various social phenomena (e.g., what causes a return to crime). However, they have difficulty providing convincing reasons for the phenomena, or they are unable to provide sound evidence for reasons they provide. It thus appears that unless educators pay special attention to nurturing students’ argumentation skills, the development of such skills will not occur. Argumentation that occurs in a group context has the decided advantage of helping participants understand their position better as they begin to re-evaluate it in the broader, fleshed out information space of counterarguments, rebuttals, and alternative assessments of evidence. Thus we have the saying that “the difficult part in an argument is not to defend one’s opinion but rather to know it” [2]. Argumentation that occurs in a collaborative learning context has the additional advantage of getting students to engage in the processes of collaborative meaning making and the co-construction of knowledge as teams of students engage in debate about important issues. Ramage, Bean, & Johnson [3] have identified six types of arguments. Our analysis of these six types reveals that they can be categorized into three main categories that we call debate, causal argumentation, and problem solving. In terms of educational usefulness, the

134

K.S. Hong et al. / The Impact of Structured Discussion on Students’ Attitudes

latter two stand out. In the current study, causal argumentation was chosen as the basis of our research on students’ acquisition of argumentation skills. Causal arguments try to show how one event brings about another and require close analysis of phenomena. A key purpose in exposing students to the process of causal argumentation is to help them understand the nature of scientific knowledge: that it is not simply a bundle of “discovered” or “proven” facts [4]. Rather, in education, students should be helped to understand that received knowledge involves a social process of “knowledge making” that is centrally grounded in argumentation. In the current study, a class of middle-school students engages in causal argumentation over a period of four weeks. A combination of online and offline learning activities was designed to introduce the students to causal argumentation. In this paper we investigate the students’ change in attitudes towards the causal argumentation learning activities as they engage in a web-based structured argumentation board and offline lessons. The paper proceeds with a review of relevant previous work and a description of the design of the research program and methods used for the current paper. Finally, we present the quantitative findings on the students’ attitudes. Qualitative analysis was carried out through the use of pre-post student essays, analysis of the forum messages and interviews. However, it will be reported separately and will not be discussed in this paper.

2. Previous Work There has been considerable work in the field of CSCL (Computer Supported Collaborative Learning) that focuses on student “conversation” in electronic discussion boards. Such discussions are typically threaded, allowing students to respond using free-form text to any particular message within a thread of messages. In such discussion boards, student dialog can be extremely rich. However, due to the lack of structure (other than threading), the dialog often ends up being difficult to follow and to analyze [5]. Two broad approaches have been used to try to improve on the quality of students’ electronic discourse. The first approach seeks to enhance students’ articulations through the use of sentence openers and prior classification of a student’s response. These features, evident in CaMILE [6] and CSILE [7] can encourage more critical and reflective thinking on the part of students despite the fact that some students will feel restricted by these features. A second approach seeks to enculturate students into a particular way of thinking by imposing “constraints” on what students can choose to do (or say) at any point in time. Belvedere [8] is an exemplar of such a system, although it is a diagrammatic representation tool rather than a textual one. Cho & Jonassen [9] were able to use Belvedere to explore students’ argumentation based on the constraints (i) hypothesis, (ii) data, (iii) principles, and (iv) other, and using the relations (i) for, (ii) against, and (iii) and. In subsequent work, Jonassen & Remidez [10] describe initial efforts in building a structured discussion board that implements arguments at four levels: (i) problem, (ii) proposal, (iii) warrant, and (iv) evidence. This scheme does not actually implement the complete Toulmin Argument Pattern which includes the elements datum, claim, qualifier, warrant, backing, and rebuttal. Research on constraint-based approaches directed at supporting the acquisition of argumentation skills is still in its infancy. Weinberger, Fischer, & Stegmann [11], for instance, get students to use the interface that uses an incomplete form of Toulmin’s Argument Pattern (like Jonassen & Remidez, [10]). The interface also does not separate ground from warrant; thereby introducing analytical difficulties when teachers (or researchers) try to determine what is the student’s intended ground and what is the intended

K.S. Hong et al. / The Impact of Structured Discussion on Students’ Attitudes

135

warrant. In addition, the representation is potentially confusing. While it adheres to the forward reasoning scheme leading to a claim that Toulmin describes, this scheme does not naturally support causal argumentation which starts with claims which are then debated (unlike the legal reasoning process that ends with a claim: a declaration of guilt or otherwise). Research to date on argumentation systems appear to suffer from at least two limitations. The first limitation relates to partial or selective implementation of the Toulmin Argument Pattern, together with sometimes confusing use of terms. Following Ramage et al. [3], a ground, strictly speaking, refers to the evidence brought to bear to support a justification for a claim, not the justification itself. The second limitations relates to the confusing way in which the elements of argumentation are often represented. There is a third area of concern. Research findings to date have not always yielded expected results. Cho & Jonassen [9], for instance, cite a study by Tan [12] showing that students using a constraint-based argumentation tool (QuestMap™) performed significantly better in stating “grounds” in their argumentation but this had no significant effect on students’ problem solving skills. Furthermore, findings from Jonassen & Remidez [10] suggest that students were not always choosing argument elements reliably and accurately.

3. Research Objectives Our analysis of the previous body of research led us to the belief that promoting the practice of argumentation requires the development of appropriate pedagogical strategies and materials that offer practical guidance to the teachers. Furthermore, due to the importance of engaging the students in cooperative and collaborative dialogical group argumentation we decided to develop a Web-based argumentation tool. Therefore our main research objectives are: 1. Develop understanding of and principles for classroom interventions related to fostering students development of critical thinking and argumentation skills using design-based research. 2. Develop and enhance a Web-based argumentation tool for group argumentation. 3. Investigate methods of effective student assessment using discourse-based qualitative methods. 4. Develop collaborating teachers’ abilities to design their own lesson plans for continued use of the Web-based argumentation tool. The focus of the study reported here is, however, principally on the first area of interest. We investigate the changes in students’ attitudes towards argumentation before and after they engage in causal argumentation learning activities.

4. Theoretical Framework In the current study the underlying argumentation framework is that of Toulmin’s Argument Pattern [13]. According to Toulmin [14], an individual argument consists of a statement or claim which can be supported by grounds or data while a warrant can be used as justification for the claim. Toulmin also recognizes three other elements that may be present in an argument backing, qualifier, and rebuttal. Backing provides credibility for the warrant and comes in the form of evidence and data. A qualifier indicates the degree of force or certainty that a

136

K.S. Hong et al. / The Impact of Structured Discussion on Students’ Attitudes

claim possesses and is sometimes implicit in the structure of an argument by the use of the words like “maybe” or “might”. A rebuttal represents certain conditions or exceptions under which the claim will fail [13]. Ramage et al [3] added another element called reason to link the grounds to the claim while a warrant identifies the underlying assumptions behind making the claim and/or reasons supporting it. We adopted an instructional approach that scaffolds the learning process through the use of modeling the argumentation steps and sentence openers for students to compose their arguments. The argument is made that, over prolonged use, the scaffolds will be internalized by students as a “cognitive residue” [15] so that when the scaffolds are removed, students’ habits of thinking with respect to argumentation and its structure will remain. Addressing the confusing use of terms, the intervention was designed to introduce these terms to students before they were used in the online structured argumentation board. This reduced chances of students selecting an inappropriate argumentative move due to their misunderstanding of the terms used. Differing from Weinberger et al [11], where grounds are combined with the warrant as one element, the online tool will represent Toulmin’s elements individually. This provides students with a clearer definition of Toulmin’s Argument Pattern and aids in clearer post-intervention analysis. For example, if grounds and warrants were combined as one entry, it might be hard to find out whether the student understood the difference between grounds and warrants through their responses using such an argumentation tool.

5. Research Design The current study takes the form of a design experiment with two cycles. Design experiments, as a research methodology, emphasize the detailed implementation and study of interventions with evolving pedagogical goals in rich, authentic settings. It acknowledges the complexities of classroom teaching and enlightens both practitioners and researchers by leading to the development of theoretical ideas grounded in contexts of practice. At the current stage, the study is in the first intervention which consists of a combination of classroom activities and activities using the structured argumentation board. The study follows the students’ development and the activities in the learning environment. 40 fourteen-year old students participated in the intervention which was infused into the regular English language curriculum. Before the intervention started, the students were tasked to write an essay on a causal argumentation topic. A survey that measured their self-reported attitudes towards argumentation was also administered. The essay and survey constituted the pre-intervention comparison data. They went through a series of classroom lessons introducing them to elements of Toulmin’s argument pattern. Due to the limited curriculum time, the warrant and backing elements are planned to be introduced to the students at a later intervention cycle. The students were given a topic to discuss about and went through tasks to (i) develop claims of their own, (ii) support their claim with reasons, (iii) provide evidence for their reasons and (iv) to develop rebuttals against another student’s claim. Throughout the classroom activities, they were exposed to the use of appropriate sentence openers for each Toulmin element through the use of structured worksheets and the teacher modeled the argumentation process. The sentence openers also appear as scaffolds in the online structured argumentation board. After a week and a half of classroom activities, the students used the online structured argumentation board to discuss about the topic they covered in class to get themselves familiarized with the online environment. A new discussion topic was given and the class

K.S. Hong et al. / The Impact of Structured Discussion on Students’ Attitudes

137

was split into teams. A jigsaw activity structure was used. Teams were seeded with different views on the discussion topic and each team member was assigned one of five discussion forums to participate in. Teacher facilitation was faded back to allow the students to engage in argumentation without support. After the intervention, the students were tasked to write another essay on a causal argumentation topic that was similar to the pre-intervention essay. The post self-report attitude survey was also administered to the students. A second survey was also done one day after the post survey. In this survey, the items were identical to the pre and post surveys. However, students were asked to respond twice to each item on the second survey. The first response required students to report their behavior or understanding as a result of the intervention (“after”). The second required participants to report their behavior before the program (“before” rating). This second survey was administered due to the possibility of a “response shift bias” phenomenon [16],[17],[18]. In using self-report measures, researchers assume that a person’s standard for measurement of the scale being used will not change from pretest to posttest. If the standard of measurement were to change, however, the posttest ratings would reflect this shift in addition to the actual changes in the person’s level of functioning. For example, the students might feel at pretest that they are “average” in terms of their attitudes and understanding of argumentation. As the intervention progresses, their understanding of the skills involved in argumentation improve. Finally, at the end of the intervention they realize that their level of understanding was really below average at the pretest. Consequently, comparisons of pretest with posttest ratings would be confounded by this distortion of the internalized scale and would encounter a lack of findings of significant differences between pre and posttest measurements.

6. Results The statistical results of the first survey (see Table 1) showed a general increase, from preto post-intervention, in the means across the five items used to measure students’ attitudes towards argumentative practice. With the use of univariate ANOVA procedure, it is found that only the second item, Attitude 2, showed significant increase from pre- to post-intervention, with its F-ratio value as 6.127 and p-value as .016. In the second set of items used to measure how much students value the skills of argumentation, none showed a significant increase from pre- to post-intervention even though all the means have increased except for the item Value 3 where the means remained constant throughout the intervention. Next, three of the items measuring the argumentative disposition of students to provide reasons and evidence showed a general increase from pre- to post-intervention. Item Evidence 1 remained constant while item Evidence 4 decreased insignificantly over the intervention. None of the items in this category showed significant difference over the intervention, as can be seen from the F-ratio values and p-values. Lastly, the only items measuring how much students enjoy learning the skills of argumentation that showed significant increments over the intervention are Enjoy 1 and Enjoy 2, with F-ratio values (and p-values) of 7.769 (p = .007) and 6.686 (p = .012). The other two items Enjoy 3 and Enjoy 4 also showed slight increase from pre- to post-intervention, while the last item Enjoy 5 dipped slightly.

138

K.S. Hong et al. / The Impact of Structured Discussion on Students’ Attitudes

Item Attitude 1 Attitude 2 Attitude 3 Attitude 4 Attitude 5 Value 1 Value 2 Value 3 Value 4 Value 5 Evidence 1 Evidence 2 Evidence 3 Evidence 4 Evidence 5 Enjoy 1 Enjoy 2 Enjoy 3 Enjoy 4 Enjoy 5

Table 1: Statistical Results for First Survey Pre-Intervention Post-Intervention n M SD n M SD 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36 36

3.81 4.33 4.42 4.64 5.06 4.36 4.72 4.61 3.94 4.64 4.89 4.31 4.61 4.25 4.22 3.81 3.33 4.06 4.39 4.83

1.009 .828 .806 .867 .532 .931 .849 .903 .715 .762 .820 .889 .934 .806 .760 .856 .956 .826 .871 .697

38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38

4.05 4.74 4.68 4.87 5.18 4.37 4.82 4.61 4.21 4.76 4.89 4.39 4.71 4.11 4.39 4.42 3.92 4.37 4.47 4.76

.899 .554 .662 .665 .730 .751 .766 .823 1.018 .852 .649 .718 .802 .764 .916 1.030 .997 1.149 .762 .852

ANOVA F p 1.240 6.127* 2.445 1.644 .744 .001 .248 .001 1.677 .436 .001 .227 .242 .629 .772 7.769** 6.686* 1.791 .199 .149

.269 .016 .122 .204 .391 .970 .620 .977 .200 .511 .973 .636 .624 .430 .382 .007 .012 .185 .657 .700

Note. *p < .05, **p < .01

The statistical results of the second survey (see Table 2) showed that the students improved in their attitudes towards argumentation based on the five items used, as shown in the increase in the means. The univariate ANOVA results also showed that all five items had a significant increase. For the scale of valuing the skills of argumentation, there was a significant increase in the rating of all but one item, Value 2. All the items measuring the argumentative disposition of students in finding reasons and evidence to support their arguments also showed a significant increase. Similarly in the next category of items measuring the extent to which students enjoy learning the skills of argumentation, the ratings before and after the project showed significant increments. Lastly, the students also rated significant increments in their attitudes towards the project, based on the three items used in this category.

Item Attitude1 Attitude2 Attitude3 Attitude4R Attitude5 Value1 Value2R Value3 Value4R Value5

Table 2: Statistical Results for Second Survey Before After n M SD n M SD 40 40 40 40 40 40 40 40 40 39

3.20 3.55 3.95 3.63 4.08 3.40 4.05 3.90 3.40 4.21

1.043 1.239 .932 1.148 1.185 1.105 1.377 .871 1.355 .978

40 40 40 40 40 40 40 40 40 39

4.30 4.23 4.95 4.65 5.18 4.63 4.45 4.98 4.10 5.08

.966 1.423 .783 1.167 .675 .705 1.339 .733 1.297 .839

ANOVA F p

23.954** 5.118* 26.990** 15.688** 26.018** 33.778** 1.528 33.992** 5.184* 17.842**

.000 .026 .000 .000 .000 .000 .220 .000 .026 .000

139

K.S. Hong et al. / The Impact of Structured Discussion on Students’ Attitudes

Evidence1 Evidence2R Evidence3 Evidence4R Evidence5 Enjoy1 Enjoy2R Enjoy3 Enjoy4R Enjoy5 Project1 Project2 Project3

40 40 40 40 40 39 40 40 40 40 40 40 40

3.75 3.35 3.95 3.23 3.50 3.72 2.98 3.38 3.65 3.78 3.53 3.18 3.40

1.127 1.145 1.037 1.165 1.038 1.099 1.230 1.213 1.312 1.230 1.012 .958 1.236

40 40 40 40 40 39 40 40 40 40 40 40 40

4.95 4.43 5.03 4.15 4.23 4.69 4.10 4.68 4.60 4.98 4.70 4.28 4.95

.959 1.217 .891 1.424 1.165 1.080 1.297 1.023 1.081 .733 .939 1.109 .959

26.304** 16.558** 24.738** 10.106** 8.634** 15.597** 17.821** 25.457** 10.253** 27.468** 28.958** 22.539** 39.245**

.000 .000 .000 .002 .004 .000 .000 .000 .002 .000 .000 .000 .000

Note. The items with an ‘R’ have been negatively phrased compared to the original survey. *p < .05, **p < .01

7. Discussion The statistical results from the two surveys indicate a “response-shift bias” in the first survey. The first survey shows a general increase in the means on most items even though most of these items are not shown as significant in the analysis of variance. In the second survey there is also a general increase in means, however, the analysis of variance shows a stronger effect than the first survey. Response-shift bias explains this as the students have a different understanding of the meaning of the questions in the questionnaire before participating in the study as compared to after they have participated in the study. In order to investigate further, 2 focus groups were held with a sample of 12 students, 6 students in each group. The focus group responses showed that the students valued learning the structure and process of writing a causal argumentation. They explained that learning and understanding this structure and process enabled them to better understand the process and views when they encounter argumentation in the media or other places. They also felt that learning the structure and the peculiar process of causal argumentation helped them to write better in other school work which involved a structured organization. The students’ understood the need for evidence but experienced difficulty in using and finding evidence to support their own arguments. This was despite students being provided with a ready selection of relevant evidence. This indicates that the students do understand the importance of evidence, but find it to be a major obstacle in their writing of arguments. The students expressed positive attitudes, values and enjoyment towards using an online structured argumentation board as compared to an offline face-to-face classroom argumentation activity like a verbal debate. They explained that they were able to concentrate better on developing a good argument in the online asynchronous environment. Noise in the classroom and the immediate “on-the-spot” responses required in a verbal debate impeded their ability to concentrate and construct better arguments. The researchers’ observations were congruent to the students’ views. When the students worked offline in the class, the noise level in the class was extremely high. However, when the class was engaged in a discussion using the online argumentation tool, the class was very quiet and the individual students were highly focused on the task. The students also liked the ability to follow other students’ arguments online as they can see other students’ work and learn from it. The students also mentioned that the online argumentation environment provided a more comfortable environment as compared to

140

K.S. Hong et al. / The Impact of Structured Discussion on Students’ Attitudes

“face-to-face”. This could be due to the emotional tension felt when students are expressing conflicting viewpoints against each other “face-to-face”. The students’ responses did not indicate any difficulties in using the online structured argumentation board. They found it easy to learn, taking one to five minutes to understand the interface. The sentence-openers and pre-set argumentation post selections (claim, reasons, evidence, rebuttal etc.) were also easily understood.

8. Conclusion A four week intervention on causal argumentation with a class of middle-school students was reported. The students’ showed changes in attitude toward the process of argumentation after accounting for response shift bias. We attribute this effect to the students going through the intervention and developing a deeper sense of what argumentation is about. Interviews with students showed that they enjoyed the web-based structured argumentation board as well as the intervention in general. Qualitative analysis of student essays is still ongoing and will be reported separately.

References [1] Kuhn, D. (1991). The Skills of Argument. Cambridge University Press. [2] Kirschner, P. A., Buckingham Shum, S. J., & Carr, C. (eds.) (2003). Visualizing Argumentation: Software Tools for Collaborative and Educational Sense-Making. Springer. [3] Ramage, J. D., Bean, J. C., & Johnson, J. (2004). Writing Arguments: A Rhetoric with Readings (6th edn.). Pearson Longman. [4] Driver, R., Newton, P., & Osborne, J. (2000). Establishing the norms of scientific argumentation in classrooms. Science Education, 84 (3), 287–313. [5] Tay, M. H., Hooi, C. M., & Chee, Y. S. (2002). Discourse-based learning using a multimedia discussion forum. In Proceedings of the Tenth International Conference on Computers in Education, Auckland, New Zealand, pp. 93–94. IEEE Computer Society. [6] Guzdial, M. & Turns, J. (2000). Computer-supported collaborative learning in engineering: The challenge of scaling-up assessment. In M. J. Jacobson & R. B. Kozma (eds.), Innovations in Science and Mathematics Education, pp. 227–257. Lawrence Erlbaum. [7] Scardamalia, M. & Bereiter, C. (1994). Computer support for knowledge-building communities. Journal of the Learning Sciences, 3 (3), 265–283. [8] Suthers, D. (1998). Representations for scaffolding collaborative inquiry on ill-structured problems. Paper presented at the 1998 AERA Annual Meeting, San Diego, CA. [9] Cho, K. L. & Jonassen, D. H. (2002). The effects of argumentation scaffolds on argumentation and problem solving. ETR&D, 50 (3), 5–22. [10] Jonassen, D. & Remidez, H. Jr. (2005). Mapping alternative discourse structures onto computer conferences. Int. J. Knowledge and Learning, 1 (1/2), 113–129. [11] Weinberger, A., Fischer, F., & Stegmann, K. (2005). Computer-supported collaborative learning in higher education: Scripts for argumentative knowledge construction in distributed groups. In Proceedings of CSCL 2005. [12] Tan, S. C. (2000). Supporting collaborative problem solving through computer-supported collaborative argumentation. Unpublished doctoral dissertation, Pennsylvania State University. [13] Toulmin, S. (1958/2003). The Uses of Argument (updated edn.). Cambridge University Press. [14] Toulmin, S., Rieke, R. and Janik, A. (1984). An Introduction to Reasoning. New York: Macmillan. [15] Zellermayer, M., Salomon, G., Globerson, T., & Givon, H. (1991). Enhancing writing-related metacognitions through a computerized writing partner. American Educational Research Journal, 28, 373–391. [16] Howard, G. S. (1980). Response shift bias - a problem in evaluating interventions with pre/post self-reports. Evaluation Review. 4(1), 93-106. [17] Rockwell, S. K. & Kohn, H. (1989). Post-then pre evaluation. Journal of Extension. 27(2), 19-21. [18] Rohs, F.R. & Langone, CA. (1997). Increased Accuracy in Measuring Leadership Impacts. Journal of Leadership Studies. 4(1), 150-159.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

141

Assessing the Impact of A Structured Argumentation Board on the Quality of Students' Argumentative Writing Skills Ole C BRUDVIK, Khai Seng HONG, Yam San CHEE, Libo GUO National Institute of Education, Nanyang Technological University, Singapore [email protected] Abstract: This paper focuses on design of qualitative discourse-based methods for effective assessment of students’ quality of argumentation. We advance a method for qualitative assessment of students’ argument which we through discourse-based methods evaluate the speaker’s stance in the argument, the speaker’s thematic development of the content and criteria for layout. Our research program takes a design-based research approach focusing on the design and evaluation of Web-based learning environments that support the teaching and development of student’s critical thinking skills and disposition through development of the skills of sound argumentation. The purpose of the research is to develop materials and strategies to support argumentation in the classroom with a Web-based tool for group argumentation, and to support the teachers’ developing skills for teaching argumentation. Data was collected pre and post and during a four week intervention. Students causal argumentation essay were collected both pre and post. The paper presents the method with an analysis of one student’s causal argumentation essays before and after the intervention. Keywords: Critical thinking, argumentation, qualitative assessment, ICT, structured argumentation board

Introduction A key skill for critical thinking is that of argumentation. To argue effectively, students must first be able to articulate their thinking, and then also be able to reflect on that thinking. Kuhn’s [1] study of argument-based reasoning across the lifespan found that, I everyday reasoning, people readily make assertions about what causes of various social phenomena (e.g., what causes students failure in school). However, they experience difficulty providing cogent reasons for the phenomena or they find themselves unable to provide sound evidence for the reasons they give. In her more recent book, Kuhn [2] further argues that the most enduring and valuable skills that educators can impart to school children are the skills of inquiry and argument. Unless educators pay special attention to nurturing students’ argumentation and reasoning skills, it appears that the development of such skills will not occur. Traditional methods for assessment of students argumentation has mainly focused on the criteria for layout of the argument, evidence and explanations [3] and [4], in this paper we advance a method for qualitative assessment of students’ argument which we through discourse-based methods evaluate the speaker’s stance in the argument, the speaker’s thematic development of the content, in addition to criteria for layout. In the current study, a class of middle-school students engaged in causal argumentation over a period of four weeks. A combination of online and offline learning activities was designed to introduce the students to causal argumentation. A web-based

142

O.C. Brudvik et al. / Assessing the Impact of a Structured Argumentation Board

structured argumentation board was used for the online argumentation. The paper proceeds with a review of relevant previous work critical thinking and argumentation, a brief overview of the research program, our theoretical framework and analysis of one student’s causal argumentation essays before and after the intervention. 1. Critical Thinking and Argumentation While there is a long-standing literature on thinking [5], critical thinking [6] and critical reasoning [7], traditional approaches to the subject have focused on critical thinking in a logico-deductive framework that emphasises the importance of argum`ent validity and the need to detect and avoid fallacies in reasoning (e.g., Munson, Conway & Black, 2004). All too often, students end up learning about critical thinking rather than acquiring the skills of critical thinking. As Kuhn [2] emphasises, however, “[t]he concept of thinking skills adopted here contrast sharply with [the] traditional one. Thinking is something people do, most often collaboratively, while they are engaged in pursuing the activities and goals that fill their daily lives” (original emphasis; p. 13). Dialogue is not simply talk or the sharing of ideas. It is a structured, extended process leading to new insights and deep knowledge and understanding and, ultimately, better practice. There is a strategic orientation implicit in dialogue aimed at advancing beyond participants’ initial stages of knowledge and belief. Argumentation that occurs in group context has the decided advantage of helping participants understand their position in a critical light as they begin to re-evaluate it in a broader, fleshed-out information space of counterarguments, rebuttals, and alternative assessment of evidence. Students engaged in the process of argument are given the opportunity to understand that “the difficult part in an argument is not to defend one’s opinion but rather to know it” Andre Maurois, 1885-1967, cited [8]. Argumentation that occurs in a collaborative learning context has the additional advantage of getting students engaged in the process of collaborative meaning-making and co-construction of knowledge as teams of students engage in dialogue and debate. Through this process, students are helped to understand that “any true understanding is dialogical in nature” [9] as they appropriate the multiple voices participating in the dialogic space. In addition, students also come to “know themselves” and construct a sense of personal identity [10] through the practice of argumentative discourse. 2. Research Programme The current study takes the form of a design experiment with two cycles. Design experiments, as a research methodology, emphasize the detailed implementation and study of interventions with evolving pedagogical goals in rich, authentic settings. It acknowledges the complexities of classroom teaching and enlightens both practitioners and researchers by leading to the development of theoretical ideas grounded in contexts of practice. 2.1 Research Objectives Our analysis of the previous body of research led us to the belief that promoting the practice of argumentation requires the development of appropriate pedagogical strategies and materials that offer practical guidance to the teachers. Furthermore, due to the importance of engaging the students in cooperative and collaborative dialogical group argumentation we decided to develop a Web-based argumentation tool. Therefore our main research objectives

O.C. Brudvik et al. / Assessing the Impact of a Structured Argumentation Board

143

are: 1. Develop understanding of and principles for classroom interventions related to fostering students development of critical thinking and argumentation skills using design-based research. 2. Develop and enhance a Web-based argumentation tool for group argumentation. 3. Investigate methods of effective student assessment using discourse-based qualitative methods. 4. Develop collaborating teachers’ abilities to design their own lesson plans for continued use of the Web-based argumentation tool. The focus of the study reported here is, however, principally on the third area of interest. 2.2 Intervention 40 middle-school students participated in the intervention which was infused into the regular English language curriculum. Before and after the intervention, the students were asked to write an essay on a causal argumentation topic. Classroom lessons introduced them to elements of Toulmin’s argument pattern [11] & [12]. Due to the limited curriculum time, the warrant and backing elements are planned to be introduced to the students at a later intervention cycle. The students were given a topic to discuss about and went through tasks to (i) develop claims of their own, (ii) support their claim with reasons, (iii) provide evidence for their reasons, and (iv) to develop rebuttals against another student’s claim. Throughout the classroom activities, they were exposed to the use of appropriate sentence openers for each Toulmin element through the use of structured worksheets and the teacher modeled the argumentation process. The sentence openers also appear as scaffolds in the online structured argumentation board. After a week and a half of classroom activities, the students used the online structured argumentation board to discuss about the topic they covered in class to get themselves familiarized with the online environment. A new discussion topic was given and the class was split into teams. 3. Theoretical Framework for Assessing the Quality of Students Argumentation Drawing upon previous work at the analysis of students’ argumentation [11] and [12], we propose a framework which involves an examination of the stance the student takes toward the content presented, the depth and strategies of the development of the content, and the layout of the argument. Each of these is described briefly below. In order to assess the speaker’s stance in the argument we draw upon Halliday and Matthiessen’s [13] modality system. The first element is that of polarity (‘yes’ and ‘no’). For example, “I liked Yen” (positive) and “I didn’t like Yen” (negative). When a text consists only of such sentences, the author takes an authoritative stance toward the content of the text and tends to close off the opportunity for negotiation and dialogue with the reader. Such a text does not encourage alternative points of view and is likely to become a target for attack on its tendency to over-generalize. The second element is the space between ‘yes’ and ‘no’, the Modality [13]. There are four types of modality. These are explained below. (i) Probability: where the speaker expresses judgments as to the likelihood or probability of something happening or being, ‘maybe yes or maybe no’. For example,

144

O.C. Brudvik et al. / Assessing the Impact of a Structured Argumentation Board

‘The bridge was possibly built in the 1920s’, ‘The bridge must have been built in the 1920s’. In both examples, the author is tentative and allows alternative judgments to be given by the reader; (ii) Usuality: where the speaker expresses judgments as to the frequency with which something happens or is, ‘sometimes yes sometimes no’. For example, ‘he usually sits there all day’; (iii) Obligation: where one gets other people to do things in a less direct way, through using a way other than the imperative ‘Get out of here!’, for example. Examples: ‘We must read “The Bostonians”’, ‘You are obliged to read Henry James!’; and (iv) Inclination (& Ability): willing / able to do something (for someone else). For example, ‘I’d like to lend you “The Bostonians”’. For the analysis of the speaker’s strategy for developing the thematic content and underlying ideology we draw upon Lemke’s [14] semiotic thematic system analysis. Semiotics describes social actions in terms of semiotic resources and semiotic formations. Semiotic resource systems matches the kinds of meanings you can make (semantic functions) with the actions (such as words) needed to make those meanings in a particular community [14]. Semiotic formation is an actual pattern of meaningful action, using semiotic resources that is repeatedly performed and recognized in a community [14]. Activity structures and thematic patterns (more properly called thematic formations) are examples of semiotic formations. A record of social action, whether piece of writing, video etc., is a semiotic text. The actual events constitute a semiotic production [14]. In the current study, we are analyzing a semiotic text in terms of the thematic pattern of the content. A thematic pattern is a way of picturing the network of relationships among the meanings of key terms in the language of a particular subject. Those terms and their synonyms amount to ways of saying the thematic items of the pattern. The grammar and rhetorical forms used in speaking or writing provide the means for expressing the semantic relationships among these items. For criteria for layout we draw upon Toulmin [11]. According to Toulmin [11] and [12], an individual argument consists of a statement or claim which can be supported by grounds or data. A warrant is used as a justification for the claim. In addition, backing, qualifier, and rebuttal are optional elements that make up an argument. Backing provides evidence and data for the warrant while a qualifier indicates the degree of force or certainty that a claim possesses. Qualifiers are similar in nature to Halliday’s modality system [13]. A rebuttal represents certain conditions or exceptions under which the claim will fail [11]. Ramage et al [15] adapted Toulmin by added another element named reason to link the grounds to the claim and re-defining the warrant as the underlying assumption behind making the claim and/or the reasons supporting it. 4. Data Analysis – Speakers Stance The two topics ‘what causes students to make friends more easily in your school?’ and ‘what causes teenagers in Singapore to stay up till past mid-night on a regular weekday?’ invite students to identify and hypothesize the causes for some phenomena and justify their hypotheses. Given the complexity of student and adolescent lives and the limitation of individual’s observation, the students will of necessity adjust the contents they put forward, unless they decide to include (a) some accounts about themselves, their siblings and other close relationships, and (b) some accounts that they believe they can present as facts, that do not need a modification or qualification. By examining the modal choices that a student makes we can judge their awareness of the audience and hence also their own selves, and their facility with these interpersonal resources.

O.C. Brudvik et al. / Assessing the Impact of a Structured Argumentation Board

145

4.1 Analytical procedure Following Halliday and Matthiessen [13], we first segmented the text into ranking clauses and then established whether or not each clause is polarized or takes a modal element. If the clause is of the latter type, we further identified the type of modal element presented briefly above. We also included as instances of modal elements separate clauses (interpersonal metaphor, [13]) and group or group complexes, in accord with the observation that interpersonal meaning is prosodic and may appear in various places and in various forms. In what follows we describe how one student employs modal resources to present his arguments. 4.2 Analysis of one pair of essays Within the 32 ranking clauses in the pre-intervention essay, 15 modalities were identified (will, it is known, usually, may, we also know, usually, definitely, will, definitely, able to, we also know, will, probably, may, can). This results in a 46.9% (15/32) ratio of modality to ranking clauses. Overall, the student took care to modify his statements through the use of modal elements, such as ‘may’ and ‘probably’. He was well aware of the presence of alternative points of view. For example, ‘Usually, people of our ages may make friends easily as we are more outspoken and more daring to approach’. The use of ‘usually’ and ‘may’ suggests that the student has taken a balanced, non-dogmatic view of the content of the clauses. Also, the student is able to make conscious selections of the modal resources to achieve the specific effects that he intends. For example, in ‘we students will definitely meet up everyday, and therefore…’, ‘will definitely’ brings about a specific effect and suits the content of the clause well. In the post-intervention essay, 36 ranking clauses were found. Within these clauses, there were 23 modalities (definitely, it is known, definitely, from my own experience, will, always, in my opinion, unable to, can, from what I see, unable to, really, will, from my personal experience, always, it is known, personally, can, from what I think personally, will, have to, will, the conclusion). This results in a 63.9% (23/36) ratio of modality to ranking clauses. Compared with the pre-intervention essay, the student attempted more modal elements and exhibited a larger variety of them as well. More significantly, the student included more mention of ‘my personal experience’ as a way to both beef up the validity of the content of his clauses and make his statements less prone to attacks from critical readers. ‘My personal experience’ was a sentence opener scaffold introduced to the students in the web-based argumentation board. However, it would take more practice for him to be equally skilled in both form and function. For instance, the structure for ‘in my opinion of staying till midnight’ is incorrect. 5. Data Analysis – Thematic Development 5.1 Analytical procedure In our analysis of the thematic pattern of the content we draw upon Lemke’s [14] semantic relations for thematic analysis. In thematic analysis, a semantic relation describes how the meanings of two words or phrases (thematic items) are related when they are used together in taking about a particular topic. Thus the thematic pattern shows how the semiotic text strategically develops its meanings.

146

O.C. Brudvik et al. / Assessing the Impact of a Structured Argumentation Board

5.2 Analysis of one pair of essays In the pre-essay the speaker sets up two thematic formations. The two thematic formations become clear when the speaker states: “These are some time and place factors which allows making friend in …easier”. In the first thematic formation the speaker refers to teenager’s stage in the life cycle of human being as “outspoken”, “attractive”, “daring to approach” and seem to imply that girls and boys are eager to know each other. In the second thematic formation the speaker refer to place as the school. The speaker develops the second thematic formation with the activities in the school (outings, field-trips, CCA, sports), shared religion and activities with the primary school. In the post-essay the speaker sets up two thematic formations and takes an evaluative stance to the content of the two thematic formations. The two thematic formations become clear when the speaker states: “Though these things can be controlled, there are stuff which makes one not sleeping early”. The first thematic formation is the activities teenagers themselves choose to do which causes them to stay up late (online chatting, watch television). The second thematic formation is the uncontrollable factors which cause one not to be able to sleep. The writer seems to refer to biological causes with the words “energetic” and “hyperactive” and “it is known that if a person does his workout late in the night, it affects his sleep”. The writer takes an evaluative stance to the content of the two thematic formations by constructing an alliance between them [16] the speaker normalizes and warrants the two thematic formations [16] as he develops his argument. 5.3 Comparison Compared to the pre-essay, the speaker better develops the thematic content in the post-essay. The speaker is able to develop the two thematic formations by constructing an alliance between them. In the pre-essay, the speaker does not explicitly construct an evaluative or attitudinal stance [16] to the content of both thematic formations. The speaker develops the second thematic formation (place: school) where as the first thematic formation is not developed. 6. Data Analysis – Criteria for Layout 6.1 Analytical Procedure The text is first segmented before being coded as one of the elements in Figure 1 based on its overall relation to the text as a whole. A portion of text will be used as an example: “Definitely it means less sleep each day due to the fact that teenagers are in secondary school. It is known that secondary schools definitely gives homework and projects to students. From my personal experience, homework will always be dragged till late midnight.” Analyzing the first sentence “Definitely it means less sleep each day due to the fact that teenagers are in secondary school”, the student claims that having less sleep each day is due to school. The student then continues on to support his claim by providing a reason, “school gives homework and projects”, of why he believes it is so. An additional elaboration is given in the next sentence in the form of personal experience, which acts as grounds for the student’s earlier reason.

O.C. Brudvik et al. / Assessing the Impact of a Structured Argumentation Board

147

6.2 Analysis of one pair of essays In the student’s pre-intervention essay (Figure 2), 9 claims were made, with only 5 being supported by reasons. No other major Toulmin Argument Pattern elements were found other than qualifiers. Even though a lot of claims were made, the student only gave supporting reasons for 5 of them. Even then, the reasons were not elaborated on and were no longer than one sentence each. In the post-intervention essay (Figure 3), the student made 5 claims and provided reasons for all of them. The reasons provided were also better elaborated on as compared to the pre-essay. Two counts of personal experiences were coded as evidence in the form of grounds. Two self-rebuttals were also coded in the essay. In summary, the post-essay was richer in terms of Toulmin’s Argument Pattern due to the display of a more dialogic tone through the self-rebuttals and having better elaborated reasons. The student also understood the need to back up his claims with grounds and offered two of his own personal experiences as evidence. 7. Conclusion The method for analyzing student argumentation described in this paper provides for a richer and more complete analysis in that it takes into consideration the speaker’s stance and the speaker’s strategy for developing the content in the argumentation as well as the criteria for the layout of the argument. It is our purpose to further develop this framework into an analytical tool for assessing students’ argumentation. Due to space limitations here, an exhaustive analysis at this stage is not possible. This will be provided in the presentation format. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]

Kuhn, D. (1991). The Skills of Argument. Cambridge University Press. Kuhn, D. (2005). Education For Thinking. Harvard University Press. Osbourne, J., Erduran, S., & Simon, S. (2004). Enhancing the quality of argumentation in school science. Journal of Research in Science Teaching, 41(10), 994-1020. Cho, K. L. & Jonassen, D. H. (2002). The effects of argumentation scaffolds on argumentation and problem solving. ETR&D, 50 (3), 5–22. Dewey, J. 1991. (original publication 1910). How we Think. Prometheus Books. Buffalo, NY. (Originally published by: D.C. Heath, Lexington, MA) Ennis, R. H. (1996). Critical Thinking. New Jersey: Prentice Hall. Cederblom, J., & Paulsen, D. W. (2001). Critical Reasoning. Understanding and Criticising Arguments and Theories. Belmont, CA: Wadsworth/Thomson Learning. Kirschner, P. A., Buckingham Shum, S. J., & Carr, C. S. (2003). Visualising argumentation: software tools for collaborative and educational sense-making. London: Springer Verlag. Bakhtin, M. M. (1986). Speech genres and other late essays. C. Emerson, & M. Holquist (Eds.); V. W. Mc Gee, Trans.; pp 259-422. Austin: University of Texas Press. Gee, J. P. (2001). Language in the science classroom: Academic social languages as the school based literacy. Paper presented at the Science Literacy Connections Conference, Baltimore, MD. Toulmin, S. (1958). The uses of argument. Cambridge, UK: Cambridge University Press. Toulmin, S., Rieke, R., & Janik, A. (1984). An Introduction to Reasoning. New York: Macmillan. Halliday, M. A. K., & Matthiessen, Christian M. I. M. (2004). An Introduction to Functional Grammar. New York: Oxford University Press. Lemke, J. L. (1990). Talking science: Language, learning and values. Norwood, NJ: Ablex. Ramage J., D., Bean, J.,C., and Johnson, J. (2004). Writing arguments: A rhetoric with readings. Pearson Education Inc. Lemke, J. L. (1998). Analysing Verbal Data: Principles, Methods, and Problems. In K Tobin & B Fraser, (Eds). International Handbook of Science Education (Kluwer)

148

O.C. Brudvik et al. / Assessing the Impact of a Structured Argumentation Board

Appendix – Student Essays

Claim Claim

Claim

Reason

Claim Claim Claim Claim Claim Claim

Reason Reason

Reason Reason

Figure 1: Pre-intervention Essay Toulmin Argument Pattern Analysis

Claim

Reason Ground

Claim Rebutt

Claim

Reason

Reason Ground

Claim Rebutt

Claim

Reason

Reason

Figure 2: Post-intervention essay Toulmin Argument Pattern Analysis

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

149

Incorporating Online Discussion in Classroom Learning: A New Strategy Wenli Chena, Chee Kit Looia Learning Sciences Laboratory, National Institute of Education, Singapore [email protected] [email protected]

a

Abstract: This paper explored the advantages and disadvantages of in-class online discussion by implementing online discussion in both in- and off-classroom settings for a professional development course, and by evaluating the strengths and weaknesses of such practices. Our results showed that the advantages of in-class online discussion are providing more perspectives, equalizing the participation of discussion, promoting cognitive thinking skills and in-depth information processing, and the ease of circulating and archiving files and documents. The disadvantages with in-class online discussion include lack of face-to-face interaction, the necessity to use more time, and the inefficiency accruing from the realization that not all learners will read the online postings. Keywords: online discussion, in-class discussion, face-to-face instruction

Introduction Research on online discussion in learning and education has proliferated recently, many of which documented the advantages of online discussion in teaching and learning: 1) increasing flexibility by removing time and space restrictions of the typical classroom setting (Curtis & Lawson, 2001; Harasim et al., 1995; Henri, 1992). 2) promoting discussion and collaboration among learners (Gay, Sturgill, Martin, & Huttenlocher, 1999; Pena-Pérez, 2000), 3) equalizing participation of learners (Hsi & Hoadley, 1997), 4) allowing learners to have more time to think “deeply” before posting messages in a discussion area (Moore, 2002), 5) providing students with an opportunity to see from different perspectives which may foster new meaning construction (Heller & Kearsley, 1996; Ruberg, Moore, & Taylor, 1996), and 6) recording learners’ s thoughts, reflection and debate by automatically saving the messages posted in the discussions (Hara, Bonk & Angeli, 1998). Despite the extensive research about online discussion in teaching and learning, most of them focus on exploring the use of online discussion in an off-classroom setting, as a main instruction method in distance learning, or as a supplementary teaching and learning method to face-to-face instruction. There is hardly any practice documented and research conducted on online discussion in an in-classroom setting. Is online discussion in-class a useful new teaching and learning strategy, or an impractical idea? What are the advantages and disadvantages? This study tries to answer these questions by implementing online discussion in both in- and off-classroom setting in a professional development course, and evaluating the strength and weakness of the practice towards the possibility of in-class application.

150

W. Chen and C.K. Looi / Incorporating Online Discussion in Classroom Learning

1. Instructional Design and Description of Online Discussions The online discussion group examined in this study consists of 16 Heads of Departments of Information Technology (IT HoDs) from a few Singapore schools who attended the professional development course at National Institute of Education, Singapore. Eight face-toface sessions of the course were conducted once a week, each lasting 3 hours. Online discussions were integrated both in-class and off-class. A typical teaching and learning cycle is the following: 1. Instructor briefly introduce topic at the end of previous session Within Class F2F 2. Elicit learners’ previous knowledge of target topic on XMail Off class Online 3. Instructor’s PowerPoint presentation which synthesizes the Within class F2F various ideas of the learners manifested in their online messages 4. Learners individually or form groups to post reflections on issues raised by the instructor (e.g. how their understanding of the Within class Online issue can be translated to practical implications in their schools) 5. Group presentation by learners Within class F2F 6. Instructor introduces the new topic of next session and ask the Within class F2F learners to post reflections on the new topic 7. Learners continue to discuss the old topic as well post their Off Class Online ideas on the new topic The software used in creating the online discussion group is XMail, an email-based tool that is easy to use and possesses additional capabilities of being able to automatically organize and make available information generated by group interactions. The online discussion group was created on January 2006 before the course started. Members included 16 learners and 4 instructors of the course. The messages analyzed in this study were posted during Jan 25 and Mar 20, eight weeks time overlapping with the face-to-face lectures. Overall there were 269 messages posted during the above-mentioned period. Among them about half (136) were posted during face-to-face lectures and the other half off-class (133). On average, there were 25 messages in each subgroup.

2. Methodology To examine several dimensions of incorporating online discussion in classroom, three research methods were applied in this study: a content analysis to analyze the nature and quality of the in-classroom discussion, classroom observations to gather information about the learners’ classroom performance which may affect their online discussion behavior, and an indepth interview to get the learners’ perceptions, opinions, and suggestions on the new strategy. These three methods complement each other to get a full picture of the participation pattern and effectiveness of in-class online discussion. For the content analysis, Henri’s (1992) framework was adapted. The framework analyzes data in five broad dimensions: participation, social, interaction, cognitive and metacognitive aspects. The unit of data for analysis was each “idea” as denoted in a message, instead of the message as a whole. The inter-rater reliability test result showed that the percent of agreement between the two coders was 100% on participation dimension, 69% on

151

W. Chen and C.K. Looi / Incorporating Online Discussion in Classroom Learning

interactive dimension, 79% on social dimension, 63% on the five-level cognitive dimension, 84% in the depth of information processing and 83% on metacognitive dimension. When examining the advantages and disadvantages of in-class online discussion, we compare it with its two counterparts - off-class discussion as well as in-class face-to-face discussion. The results from different methods were presented when and where appropriate.

3. Advantage of In-Class Online Discussion 3.1 The participation of discussion is equalized in class by online discussion In this study we compared the number of messages posted by different learners in class and off class, which was an indicator of chances the learners got to express themselves. As shown in Figure 1, all learners participated in the online discussion in-class, and the number of postings by each learner was similar. On the other hand, the difference in the number of postings off-class by different learners was quite apparent. One learner posted 29 messages off-class whereas two learners never posted off-class. In the contrast, during the face-to-face class discussion time, when the instructor asked learners to share their ideas, three to five learners really spoke in the face-to-face classes. Furthermore, during the 3-hour session, normally less than 10 learners expressed their ideas to their peers. The researcher found that “struggle the turn” to speak out was not an issue. Each time the oral discussion ended because no more learners would like to share. The possible reason is that some learners were “reticent” to speak in front of others, while some others were inactive to think when their peers were sharing. class

30

In-class Off-class

25

Count

20

15

10

5

0

e rg eo G a ili ec C ria lo G on ils W es m Ja e ol ic N s ni en D e in la E es m Ja an us S a el ng A

sa Li e hi op S y nn Je

x le A ny en K

Individual Learner

Figure 1. Number of postings by each learner, in-class and off-class Apparently in-class online discussions can equalize learners’ participation to a great extent, when most of them were task-oriented so that learners felt more obliged to post and they knew the instructors would synthesize their ideas later. The equalization of in-class online discussion may: 1) benefit those who are “reticent” to speak in front of others, 2) push those who are inactive in thinking to think and post more frequently.

152

W. Chen and C.K. Looi / Incorporating Online Discussion in Classroom Learning

The equalization may lie in another aspect that learners are more likely to judge their peers from the knowledge reflected in the postings instead of other factors which may be linked with their face-to-face speaking. The evidence of this statement is that the three project leaders were all active contributors of in-class discussion, and their postings were read and replied by other learners more frequently, which is an indicator of good quality of the postings. 3.2 Learners engage in extensive writing in online discussion during class In each face-to-face class, learners were given 15-30 minutes to post their thinking and reflection in the online discussion area. They were also encouraged to post even after class. The extensiveness of writing can be measured by the length of messages, which was operationalized by the number of sentences and lines (there are about 25 words per line). Table 1. Independent-Sample t Test of Length of postings and time of Postings (N=223) Number of Sentences Number of Lines

In-class Off-class In-class Off-class

N 129 94 129 94

Mean 10.64 8.14 12.40 8.68

t 1.973* 1.986*

Note: *p<.05.

Independent-sample t test was conducted to compare the length of messages posted inclass and off-class (Table 1). The result showed that the in-class messages have 2.5 more sentences than off-class messages (t = 1.973, p < .05), and 3.7 more lines (t = 1.986, p<.05). The learners engaged in more extensive writing in-class than off-class. 3.3 In-class online-discussion provides more perspectives of ideas When the in-class online discussion and the in-class oral discussion were examined in detail, it was observed that the online discussion provided more perspectives than oral discussions. For example, during oral discussion, it was very rare that learners had heterogeneous opinions towards certain issues. In one session there was an oral discussion on how Ministry of Education (MOE) could help schools do technology planning. When the instructors and MOE staffs sought oral opinions from the learners, only four learners shared their opinions, mainly focusing on the workable guide of technology planning, incubators schools as models to follow, and more sharing and communication needed. However, in online discussions, almost every learner stated their opinions, and the perspectives were expanded a lot to the financial matters and the need for experienced MOE staffs. As shared by one of the learner, Dennis, “You can see the different types of reflections online. And it’s really a variety. It’s important that we can see or hear everyone’s perspective. We need not accept everybody’s views. But it’s important that we can get a sense.” The heterogeneous perspectives of in-class online discussion may be due to the following two reasons: 1) in oral discussion, learners may be affected by other out-spoken ideas into homogeneity which may discourage them to express their ideas, and if she feels others had already expressed the same or similar ideas as her, then she may not want to use up

153

W. Chen and C.K. Looi / Incorporating Online Discussion in Classroom Learning

time to make a similar point, and 2) the Asian culture does not encourage people to openly challenge others as such practice is considered an impolite and sometimes inappropriate. 3.4 In-class online discussion contains more in-depth clarification and inference skills Scholars have long investigated the connections between writing and thinking (e.g., Vygotsky, 1962) - it is through the actual process of writing our thoughts and working them over that we really come to understand. The written record allows for in-depth clarification, revision and encourages self reflection and inference and these are important learning strategies for developing an understanding of new concepts. This study has the similar finding that online discussion, when compared to oral discussion, contains high-order cognitive thinking. In the in-depth interview, one of the online discussion participants of the study, Dennis, shared his perception towards online discussion, “I think online discussion will improve the cognitive development. Because when we type, tendency is that they have a bit more time to think through. …And when you type, you will tend to be more conscious of what you type. It sounds like you have to think twice.” A Chi-square test was conducted to examine the relationship between the cognitive skills manifested in the postings and time of postings (Table 2). The results indicated that paragraphs posted in class demonstrated more in-depth clarification (43%) than posted offclass (31%), and the in-class postings also contains higher percentage of inference skills (20%) than off-class postings (6%). The percentage of elementary clarification in paragraphs posted off-class (40%) was much higher than that of the paragraphs posted in-class (14%). Table 2. Chi-Square Analysis between Cognitive Skills vs. Time of Posting (N=644)

In-Class Off-Class

Elementary clarification 68 14.3% 77 40.5%

Category of Cognitive Skills In-depth Inference Judgment clarification 205 96 51 43.2% 20.3% 10.8% 59 12 19 31.1% 6.3% 10.0%

Strategy 54 11.4% 23 12.1%

ChiSquare

63.977**

Note: ** p< .01.

3.5 In-class online discussion contains in-depth information process The existing studies showed that one of the advantages of online discussion over face-to-face oral discussion is that it encourages a deeper level of thinking (Jusri & Lim, 2003). Information processing is closely related to cognitive skills. In the content analysis, this measure was used to classify the responses into the categories of superficial and in depth processing. Messages classified as evidence of surface level processing involved mostly examples where participants contributed information about extra resources without elaboration. Messages demonstrated deeper levels of processing involved relating new information to their experiences, critically evaluating ideas, and exploring strategies. Chi-Square test was employed to compare of level of information processing between in-class postings and off-class postings (Table 3). The analysis revealed that that paragraphs

154

W. Chen and C.K. Looi / Incorporating Online Discussion in Classroom Learning

posted within class had 20% more in-depth info processing than those posted between classes (Pearson Chi-Square = 28.003, p < .01). Table 3. Chi-Square Analysis between Depth of Info Processing vs. Time of Posting (N=644)

Within Class Between Class

Level of Info Processing Surface Processing In-depth Processing 126 348 26.6% 73.4% 91 99 47.9% 52.1%

Chi-Square

28.003**

Note: ** p< .01.

In addition, comparing with oral discussion in class, the learners felt that the online discussion is more in-depth. As shared by Gloria, “I think that online discussion will be more in depth than oral discussion. Because we would have thought about it before we typed. After some thinking, when you type it out, the content will be more substantial. But when we talk, it is sometimes quite impulsive.” The result reaffirmed that during class, learners are more likely to think “deeply” than oral discussion and off-class postings. 3.6 Ease of circulating and archiving files and documents In-class discussion provides a permanent record of learners’ thoughts and reflection. This advantage is very valuable compared with in-class oral discussions. During class, instructors used the online discussion platform to circulate slides and reading materials, and the learners also send documents to instructors and peers. On average, each learner circulated five documents, including power point slides, mind map, word document, PDF file, and video files. In the in-depth interviews, many learners mentioned the advantage of online discussion in circulating and archiving files and documents - “I think the biggest advantage of online discussion would be archiving. Like what I discussed in January will still be there. You can keep it in a way that you can go back to refer to that info that was said” (Lisa); “Whatever we have posted is also stored in the server itself so we can always refer to them” (Gloria). Dennis’s experience indicates that the recording feature of online discussion is very useful to the learners, especially for those who missed classes: “I missed the Classroom of the Future session because of the national service. But the good thing is that my friends posted their comments and reflections online. This was very useful. It’s a recorded black and white and I can see. Both the learner and absentee can see. ”

4. Disadvantages of In-Class Online Discussion 4.1 In-class online discussion lacks interaction The first disadvantage identified is that in-class online discussion lacks interactions, as compared to both oral discussion and off-class discussion. In in-class oral discussion, learners and instructors talk face-to face and interact actively. However, most of the online postings are independent postings without replying and commenting on others’ postings. Chi-square

W. Chen and C.K. Looi / Incorporating Online Discussion in Classroom Learning

155

test was employed to compare the level of interaction between in-class postings and off-class postings (Table 4). The result showed that in-class postings are less likely to comment or response to others’ posting than off-class postings (Chi-Square = 15.658, p < .01). Table 4. Chi-Square test of Level of Interaction and time of posting (N=223) Independent Statement In-class 109 84.5% Off-class 63 67.7% Note: **p<.01.

Level of Interaction Implicit commentary/ Explicit commentary/ response response 15 5 11.6% 3.9% 11 19 11.8% 20.4%

Chi-Square 15.658**

The reason why in-class online postings were less interactive than off-class postings is that learners may interact more orally when they were sitting in the classroom face-to face. The lack of interaction is one of the key disadvantages perceived by the learners. As told by Dennis, “When we are side-by-side but we cannot talk to one another and we have to talk through the computer, it will reduce the kind of social interaction. Therefore we must balance the amount of oral discussion and the amount of typing to discuss. … I am not comfortable with typing in class when we don’t even know each other. Once we know, then we will know how to respond, or to use specifically, the way to respond. That is definitely more useful.” 4.2 In-class online discussion needs more time and may be inefficient Although learners have more time to think during typing, the typing itself takes time. Too much online discussion in class may slow the progress of the class. In the class, listening is to some extent a compulsory practice for interacting with instructors and peers. It would be a problem to assure that every participant in online discussion reads all the messages, although many of them contain high-order cognitive thinking and in-depth information processing.

5. Conclusion The learners engaged in extensive writing in online discussion during the class when the learners shared their reflections, deeper thinking and experiences, even though the participation was not an assessment criterion of the course. In-class online discussion provided more perspectives as learners were able to think independently and were less reluctant to share their real feelings which might be different from the view held by others. Moreover, with in-class online discussion, the participation of discussion was equalized in two aspects – 1) everybody has an equal chance to participate and fully express his/her ideas, 2) learners judge their peers by the knowledge they articulated more than other factors, such us social status, external appearances, accent or charisma. As a result, the class became a more democratic learning environment and those “reticent” to speak in front of others were encouraged to express themselves, whereas those inactive in thinking were pushed to think and post more frequently. In-class online discussion was also found to contain more in-depth clarification, and inference skills. In addition, the in-class online discussions demonstrated

156

W. Chen and C.K. Looi / Incorporating Online Discussion in Classroom Learning

deeper levels of processing that involved relating new information to the participants’ experiences, critically evaluating ideas, and exploring strategies. Several disadvantages with in-class online discussion were also revealed by the study. First, it lacks interaction because most of the online postings were task-oriented and independent postings without replying and commenting on others’ postings. Second, too much online discussion in class may slow the progress of the class. Third, in-class online discussion does not assure every learner will read the online postings because reading online discussion was not a compulsory practice. While in-class online discussion offers the potential of encouraging participation and creating deep learning opportunities, making this happen requires good learning design and organization. Courses that include in-class online discussions as a supplement to oral discussion need to carefully integrate this activity into the overall course design, so students see it as integral to the class and not as a disassociated activity. To address the issue of lack interaction, in-class online discussion can be introduced after the learners get to know each other. The topics for online discussion during class should be carefully chosen. In this study, it was found that those novel topics (such as “Community of Practice”) and those practical topics closely relevant to their work (such as “Technology Planning”) attracted more highquality postings. In addition, as not all learners feel comfortable about in-class online discussion, and not all the postings were “heard” by all class members, the effectiveness of inclass online postings may vary from learner to learner. This design could promote selfdiscipline and requires students to take more responsibility for their own learning. References [1] Curtis, D. D., and Lawson, M. J. (2001) Exploring collaborative online learning. Journal of Asynchronous Learning Networks, 5, 1, 21-34. [2] Gay, G., Sturgill, A., Martin, W., and Huttenlocher, D. (1999) Document centered peer collaborations: An exploration of the educational uses of networked communication technologies. Journal of ComputerMediated Communication, 4, 3, unpaged. [3] Hara, N., Bonk, C., and Angeli, C. (1998) Content analysis of online discussion in an applied educational psychology course. Instructional Science, 28, 2, 115-152. [4] Harasim, L., Hiltz, S. R., Teles, L., and Turoff, M. (1995) Learning Networks: A Field Guide to Teaching and Learning Online. The MIT Press, Cambridge, MA. [5] Henri, F. (1992) Computer conferencing and content analysis. In A.R. Kaye (Ed.), Collaborative Learning through Computer Conferencing: The Najaden Papers, 115–136. Springer, NY. [6] Heller, H., and Kearsley, G. (1996) Using a computer BBS for graduate education: Issues and outcomes. In Z. Berge and M. Collins (Ed.), Computer-Mediated Communication and the Online Classroom. (Vol. III: Distance learning, pp. 129-137). Hamptom Press, NJ. [7] Hsi, S., and Hoadley, C (1997) Productive discussion in science: Gender equity through electronic discourse. Journal of Science Education and Technology, 6, 1, 23-36. [8] Moore, M. G. (2002) What does research say about the learners using computer-mediated communication in distance learning? American Journal of Distance Education, 16, 2, 65-81. [9] Pena-Pérez, J. (2000) Participation, interaction and meaning construction in a university-level course using a computer bulletin board as a supplement to regular class discussions. A case study. Unpublished doctoral Dissertation, Dept. of Education, Cornell University, Ithaca, NY. [10] Ruberg, L. F., Moore, D. M., and Taylor, C. D. (1996) Student participation, interaction, and regulation in a computer-mediated communication environment: A qualitative study. Journal of Educational Computing Research, 14, 3, 243–268. [11] Vygotsky, L. S. (1962) Thought and Language. The MIT Press, Cambridge, MA. [12] Jusri, T., and Lim, G. (2003) Significance of online teaching vs. face-to-face: Similarities and difference. Paper presented at E-LEARN 2003, the World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. Phoenix, Arizona, USA, November 7-11.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

157

Using Agents for Enhancing Learning Effects in an Advanced Discussion Forum Yuejun ZHANG, Kinshuk, Øyvind SMESTAD, Jingyu YANG and Lynn JEFFERY Department of Information Systems, Massey University, New Zealand [email protected] Abstract: Software agents have long been used in educational environments to provide learning support. This paper describes an agent-based approach in a value-added online discussion forum system to facilitate enhanced interactions. The system deploys a multi-agent architecture in which four types of agents collaborate with one another to undertake tasks delegated by teachers and students. This multi-agent architecture has enabled several tertiary educational institutions in New Zealand to build a shared discussion platform that integrates their current learning management systems. Based on the monitoring of student activities, the system presents many advanced functionalities such as formation of formal answers to repeatedly raised questions (FAQs), delivery of useful messages to appropriate individuals, and organization of study groups with compatible students. Keywords: Software agents, multi-agent systems, discussion forum, interaction

1. Introduction Educational research has long identified interaction or knowledge sharing among students as a key success factor in learning [1, 2, 3]. Online Web based discussion forums provide a means to potentially allow students in different physical locations to interact with each other. Conventional online discussion forums, however, present several issues of concern that need to be addressed. The most significant issue in existing discussion forums is the lack of a mechanism to effectively monitor student activities. Usually, knowledge sharing and learning takes place adequately in an online discussion environment, but it might be appealing if the system could monitor students’ queries, identify their needs, and accordingly provide individualized help in an autonomous way. Sometimes students may even encounter difficulties, do things wrong, or fail to actively participate. In these situations, intervention from either a human tutor or a machine entity is necessary. Unfortunately, this issue has not yet been well tackled due to the lack of face-to-face opportunities which are otherwise easily found in a traditional classroom. In the case of online discussion forums, the tutors do not have direct control over the student activities. In fact, tutors may not even be available when a student needs help. 1.1 Research purpose In this paper, we describe an agent-based approach to this issue in a value-added online discussion forum system, called electronic Question & Answer Knowledge Environment (eQuake). The underlying idea is straightforward. On one hand, agents can be used to

158

Y. Zhang et al. / Using Agents for Enhancing Learning Effects in an Advanced Discussion Forum

interact with other entities on behalf of students; and on the other hand, agents can also be used to monitor student activities on behalf of other entities. Here, “other entities” indicate human entities including students and teachers as well as machine entities such as other agents. The agents work autonomously and silently in the background, collaborating with each other to complete various tasks. When a message is posted to the forum system, for example, an agent will analyze its content and accordingly take correct actions. For another example, when the tutor is not available online, a tutor agent may take over the role. eQuake is designed as a multi-agent architecture, in which four types of agents collaborate with one another to reach the system goals. This multi-agent architecture has enabled several tertiary educational institutions in New Zealand to build a shared discussion platform that integrates their currently used learning management systems (LMS). Based on the monitoring of student activities, the system presents many advanced functionalities such as formation of formal answers to repeatedly raised questions (FAQs), delivery of useful messages to appropriate individuals, and organization of study groups with compatible students. 1.2 Paper outline The outline of the rest of the paper is as follows. We begin with section 2 by giving a brief description of agent concept and related work in the educational area. In section 3, we check the rationale for the agent-based approach in eQuake, identifying the appropriateness of this approach for the problem domain. Then we present our multi-agent architecture in section 4, with a focus on the functionalities of each kind of agents and the collaborations among them. In section 5, the implementation aspects of eQuake are illustrated by focusing on the system architecture and the current deployment across multiple tertiary institutions in New Zealand. Finally, we conclude the paper in section 6, which also discusses some future research work. 2. Agents and related work 2.1 Agent concept and properties There is no agreement in the literature on a unified definition for the term “agent”. Just as Van Dyke Parunak [4] pointed out, for some, an agent is a software piece as long as it can travel over a network; for others, an agent represents a module that takes action on behalf of a human user. For still others, agenthood means a certain minimal level of intelligence, the use of a specified inter-agent language, or the ability to manipulate explicit models of beliefs, desires, and intentions. We present here the definition cited from the work of Kinshuk, Hong and Patel [5]: “An agent is a computational entity that acts on behalf of other entities in an autonomous fashion, performs its actions with some level of proactivity and/or reactiveness and exhibits some level of the key attributes of learning, cooperation and mobility.” Agents exhibit some properties upon which a common consensus has been reached. These properties include [6]: autonomy, social ability, responsiveness, and proactiveness. In addition, several other characteristics are also potentially desired for agents: adaptability, mobility, veracity, and rationality.

Y. Zhang et al. / Using Agents for Enhancing Learning Effects in an Advanced Discussion Forum

159

2.2 Educational agents and related work Software agents have long been applied in educational environments to provide learning support. Educational agents are a class of agents that assist a user in an education-related task. Their roles and advantages have been identified by many researchers such as Whatley [7], Jafari [8] and Johnson [9]. Some benefits which are offered by educational agents and closely relate to the requirements for our eQuake system can be summarized as follows: 1) Agents can perform tasks for students and instructors in a dynamic, personal, and smart teaching and learning environment. 2) Agents can be made to work actively and adapt to students. They exhibit the ability to recognize what the student needs to accomplish, and react to the students’ input. 3) Agents can continuously operate in the background on a student’s workstation and act autonomously to suggest ways in which the student might improve performance. 4) Agents can bridge the divide between time and place. Students may be dispersed and working at times to suit themselves, while the agents keep track of the students’ progress. 5) Agents can bring together students with similar interests or needs into a discussion area where they can receive help on particular problems. 6) Agents can monitor progress, give instruction when needed, help organize students’ work, and provide feedback for tutors. According to Chou, Chan, and Lin [10], educational agents can roughly be divided into two categories. One category presents to users a computer character with human characteristics that facilitate social learning. This category is usually referred to as lifelike or animated pedagogical agents. By using gestures, gaze, facial expressions, locomotion as well as verbal exchanges, animated pedagogical agents may greatly enhance the learning environment. Examples of this category include: Steve [11], Adele [12], Herman the Bug [13], Cosmo [14], WhizLow [15], and PPP Persona [16]. Another category includes pieces of software, which work invisibly within the system by autonomously dealing with delegated tasks and actively interacting with other entities. The agents in eQuake system fall into this category. Some related work in this category includes the agents in I-Help [17], in which the personal agents care for their learners by helping them to discover useful information and/or to find “ready, willing, and able” peer learners who can aid them in overcoming problems. Another example is the context-based information agents [18] that can observe conversations among a community of learners on the Web, interpret the learners’ inputs, and then assess the current context of the session. Further example includes the agents in an effective e-learning model [19], where the Open Forum Agent can parse and analyze the student query, and search the database for answers based on the parsed tokens. All of these agents can, to some degree, monitor student activities or provide adaptive help to students. Agents in eQuake go further in enhancing learning experience by facilitating intelligent learner support and cross-institution interactions. 3. Rationale for using agents in eQuake The rational for using agency technology in eQuake can be demonstrated from two aspects: agents’ properties and eQuake’s attributes. On one hand, agents have three main characteristics presented earlier in the definition of the term:

160

Y. Zhang et al. / Using Agents for Enhancing Learning Effects in an Advanced Discussion Forum

1) Autonomous – be independent and make their own decisions without the need for any external control 2) Pro-active – persistently pursue goals 3) Reactive – respond in a timely manner to changes in their environment These properties of agents match very well with the enhanced functionalities of eQuake system. eQuake is a web-based system in which students from multiple institutions engage in a student-centered community of learning to share and exchange study and subject-related problems/solutions, join study groups and receive expert advice. The system constantly monitors the student interaction and creates profiles of students based on their interaction with other students. It tags significant or repeated issues or queries raised by students for teachers who may formulate responses in the form of FAQs or respond individually to certain students. Once an FAQ item is added, the system, based on students' profiles, identifies those students who may benefit from that FAQ and advices them accordingly. Next time, when a student raises that particular issue, the system automatically intercepts the query and redirects the student to the FAQ. Students are then able to decide whether their query has been answered or if it should go to other students for further responses. Based on student profiles, the system is also able to propose study groups among compatible students, by taking into consideration their knowledge levels and learning styles. The key point to reaching these functionalities is that student activities and any other occurrences are sensed and timely reactions are undertaken. The “reactive” property of agents is a perfect fit for this requirement. Since eQuake is an online distributed Web-based system where both students and teachers interact with the system asynchronously, the system should be able to undertake tasks without instructions or supervisions from external entities. This situation requires system autonomies, and thus the “autonomous” property of agents has a significant role to play. It is also expected that the approach to tackling the stated problems be highly robust, and the system should continue to try to achieve the goals despite failed attempts. Regarding this aspect, the “pro-active” characteristic of agents is just what we want. On the other hand, Wooldridge [20] pointed out some of the system attributes that indicate the appropriateness of an agent-based solution. These attributes are: 1) The agent environment is open, or at least highly dynamic, uncertain, or complex. 2) Agents are a natural metaphor. 3) Data, control, or expertise is distributed in several locations. 4) Legacy systems are components of the whole system. eQuake possesses all these attributes, and therefore agenthood is the most suitable choice. Firstly, eQuake is a highly dynamic and complex system. As a discussion forum, eQuake allows students to post messages and interact with each other in an unrestricted manner. We cannot have a direct control on the student actions, and we never know what a student will do in the future. Secondly, agents are a natural metaphor in eQuake. It is reasonable to create a student proxy agent for each student and a tutor proxy agent for each teacher on behalf of them to undertake the interactions. Each agent is adaptable to its client, thus personalized help as well as adaptive learning is possible. Thirdly, data and control are distributed in several locations. As seen in the system architecture in section 5, eQuake is deployed across several institutions, each of which runs a local Web server and a database server of its own. Fourthly, legacy components, or in our case, heterogeneous LMSs, are used in eQuake, as seen also in section 5. To make these heterogeneous LMSs collaborate with one another and communicate with eQuake, one logical solution is to wrap them with an agent layer.

Y. Zhang et al. / Using Agents for Enhancing Learning Effects in an Advanced Discussion Forum

161

4. Multi-agent architecture in eQuake 4.1 Architecture eQuake is a multi-agent system, in which four types of agents, namely tutor proxy agent (TPA), student proxy agent (SPA), target selection agent (TSA), and query monitoring agent (QMA), collaborate with each other to achieve the system goals. The multi-agent architecture is shown in figure 1. Agents as Web Services: (Application layer)

Presentation layer: LMS User DB

Global DB

TPA Notification

LMS Plug-In

Data layer:

TSA Forum display

WS

Dispatcher

PA QMA

WS

Instance config. data

WS

DataService Datamodel (Hibernate)

SPA

Legend:

PA – Proxy Agent TPA – Tutor Proxy Agent SPA – Student Proxy Agent TSA – Target Selection Agent QMA – Query Monitoring Agent

Data Stores DB

WS – Web Service DB – Database LMS – Learning Management System

Figure 1. Multi-agent architecture in eQuake

Logically, the eQuake system is divided into three layers: presentation layer, application layer, and data layer. The presentation layer includes the existing LMS and the local database running in each institution, which is responsible for all interactions with users. This layer also includes a plug-in interface which facilitates the communication between the existing LMS and the agents in eQuake via the Web service (WS) protocol. The data layer, as it is named, manages data storage and data access for eQuake, enabling the integration of the distributed databases. The four types of agents are seated in the application layer, which accesses the data layer and provides services to the presentation layer. Each of the agents manages to complete its own tasks, and seeks for co-operations from other agents if needed. teacher 9 create FAQ

Q/A 5 retrieve

students

6 retrieve

TSA

TPA 11 notify 8 notify if repeated query

12 forward new FAQ

1 query

2 query

SPA

7 related Q/A & FAQ 13 new FAQ & rating

10 save FAQ

FAQ

QMA

3 query type 4 update

14 update

Student profile

Figure 2. Collaborations among agents when a query is posted

162

Y. Zhang et al. / Using Agents for Enhancing Learning Effects in an Advanced Discussion Forum

To illustrate the collaboration among agents, figure 2 shows a typical scenario when a student posts a query (e.g. a question) on the forum (the numbers in brackets in following discussion are those listed for activities in figure 2). First the student proxy agent (SPA) sends a request to the query monitoring agent (QMA) for analysis on the query content (2). The QMA then returns the analysis result to the SPA (3), which in return updates the student profile (4). It also retrieves existing question/answer pairs (5) as well as already available FAQs (6) which are both closely related to the current query. The SPA displays the retrieved contents to the student (7). If the QMA finds out that the query is a repeatedly raised question and the number of repetitions has reached a predefined threshold value, it then notifies the teacher proxy agent (TPA) of this situation (8). The latter consequently prompts the teacher to formalize an FAQ based on the query (9), and saves this FAQ to database (10). At the same time, the TPA notifies the target selection agent (TSA) of the new incoming FAQ (11). Upon receiving this notification, the TSA chooses those students, according to their profiles, who may be interested in this FAQ and then forwards this FAQ to the corresponding SPA (12). The SPA then delivers the FAQ to the student (13). 4.2 Functionalities of agents Although design goals of eQuake are achieved by the collaboration among all agents, each type of agent has its specific functionalities, which are briefly summarized in the following discussion. The student proxy agent is responsible for modeling student profiles, undertaking interactions with other students and the system on behalf of the student by enabling message posting, searching/forwarding related message or answers to frequently asked questions (FAQs) to the student and so on, and monitoring the student’s activities in eQuake by determining the nature of the student’s posted message, monitoring the student’s participation frequency, identifying if the student has a poor understanding of a topic, and so on. The tutor proxy agent is, on the other hand, in charge of modeling tutor profiles and helping the tutor interact with students and other agents. Examples of the interactions include enabling the creation of FAQ items, informing the target selection agent of a new FAQ, and forwarding answers to particular students. The target selection agent mainly takes two responsibilities. The first is to select students whose profiles match a new incoming FAQ, and the second is to select students with compatible profiles so that optimal study groups can be organized. Finally, the query monitoring agent is focused on a semantic analysis of the content of every incoming message posted by students or coming from the database. Based on this analysis, it can identify repeated questions and provide services to other agents. 5. Implementation 5.1 System architecture and deployment A local eQuake server, with the above-mentioned multi-agent architecture, is installed for each institution joining in the shared forum. A user interface or plug-in part is embedded in the institution’s existing LMS, so that the LMS can communicate with the local eQuake server. A local database is maintained for each local eQuake server to store posts by local users and track data for these users. eQuake servers (the actual underlying agents) located at all institutions communicate with each other using web services to complete the delegated

Y. Zhang et al. / Using Agents for Enhancing Learning Effects in an Advanced Discussion Forum

163

interaction tasks. A central server is also used to keep a list of available eQuake forums. This is especially useful for simply connecting new institutions to the network. Figure 3 shows the existing deployment of the system in three different institutions. Each institution runs an instance of eQuake on a local server that interacts with their existing LMS server. When getting data from outside the local server, requests are sent to all connected instances. The data structure is a homogeneous distributed database structure as the type of data stored at each physical site is identical. As stated above, a small portion of the data is kept in a central location, where data about the currently available eQuake forums is maintained. For the purpose of performance and reliability, the centrally kept data is also duplicated in each running instance of the system, as it rarely changes but is often queried.

Figure 3. Example deployment with local databases 5.2 Integration with heterogeneous LMSs As the user interface of our system is based on the Moodle (http://www.moodle.org/) forum module, it can easily be installed to a Moodle system as a new module/plug-in. Another system with which we have tested the integration of eQuake is the proprietary WebCT system (Campus edition 4.1) (http://www.webct.com/). We use a simple plug-in script to connect and launch our system inside the main frame of WebCT. The reason for the choice of Moodle and WebCT as the existing system for testing the integration with eQuake is that they represent a typical example of open source and closed source LMSs respectively, and because they both are being used by institutions participating in the project. 5.3 Evaluation A preliminary user evaluation has been carried out by system developers, project participants, teachers and students at collaborating institutions. The initial test results show a very promising prospect of the agency approach in eQuake. Through the testing process,

164

Y. Zhang et al. / Using Agents for Enhancing Learning Effects in an Advanced Discussion Forum

the development team iteratively received feedback on issues related to the functionality and the usability of the system and implemented improvements. Currently a formal user evaluation by students and teachers in several New Zealand institutions is being carried out. 6. Conclusion We have introduced a multi-agent approach in an advanced discussion forum system to enhance learning effects and to facilitate intensive interactions among students from multiple institutions. Through the agents’ autonomous work and collaboration, the system can model student profiles, monitor student activities, and provide students appropriate messages as well as individualized help. Preliminary tests show that the agency approach is promising and eQuake is a helpful tool for students in gaining more knowledge and understanding within their areas of study. One future improvement to this system is to include the monitoring of pedagogies. According to different learning styles, knowledge levels, individual preferences, and cognitive abilities of the learner, different pedagogical strategies are chosen manually by human teachers or automatically by the system. The system should be able to detect the selected pedagogies and behave properly to the students so that the pedagogies can be well executed. In addition, pedagogical strategies are not constant in a learning session. They usually change in line with learner needs. An obvious fact is that a learners’ knowledge level will be improved, for example, from novice to intermediate while the learning process is proceeding. In this case, normally the pedagogical strategies should be changed accordingly. Therefore, the system should monitor the changes of pedagogies, and adaptively modify its behavior to the learner. Our next step is to use agents to realize this goal in eQuake. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20]

Brookfield, S. D. (1986). Understanding and facilitating adult learning. San Francisco: Jossey-Bass. Slavin, R. E. (1983). Cooperative learning. New York: Longman. Whitsed, N. (2004). Learning and teaching. Health Information & Libraries Journal, 21(1), 74-78. Van Dyke Parunak, H. (1998). Practical and industrial applications of agent-based systems. Retrieved November 4, 2005 from http://agents.umbc.edu/papers/apps98.pdf. Kinshuk, Hong, H., & Patel, A. (2002). Adaptivity through the Use of Mobile Agents in Web-based Student Modelling. International Journal of E-Learning, 1(3), 55-64. Wooldridge, M. and Jennings, N. R. (1995). Intelligent agents: theory and practice. The Knowledge Engineering Review, 10(2), 115-152. Whatley, J. (2004). Achieving virtual teamwork using software agents. Proceedings of the Fourth international Conference on Networked Learning 2004, 696-702. Jafari, A. (2002). Conceptualizing intelligent agents for teaching and learning, EDCAUSE Quarterly, 25(3), 28-34. Johnson, W. L. (1998). Pedagogical agents. Proceedings of the ICCE’98, vol. 1, 13-22. Chou, C. Y., Chan, T. W., Lin, C. J. (2003). Redefining the learning companion: the past, present, and future of educational agents. Computers & Education, 40(3), 255-269. Rickel, J. and Johnson, W. L. (1997). Steve: an animated pedagogical agent for procedural training in virtual environments. Proceedings of Animated Interface Agents: Making Them Intelligent, 71-76. Johnson, W. L., Shaw, E., Marshall, A., and LaBore, C. (2003). Evaluation of user interaction: the case of agent Adele. Proceedings of the 8th International Conference on Intelligent User Interfaces, 93-100. Lester, J. C., Stone, B. A., and Stelling, G. D. (1999). Lifelike pedagogical agents for mixed-initiative problem solving in constructivist learning environments. User Modeling and User-Adapted Interaction, 9, 1-44. Lester, J. C., Voerman, J. L., Towns, S. G., and Gallaway, C. B. (1999). Deictic believability: coordinating gesture, locomotion, and speech in lifelike pedagogical agents. Applied Artificial Intelligence, 13(4-5), 383-414. Lester, J. C., Zettlemoyer, L. S., Gregoire, J., and Bares, W. H. (1999). Explanatory lifelike avatars: performing user-designed tasks in 3d learning environments. Proceedings of the 3rd International Conference on Autonomous Agents. Andre, E., Rist, T., and Muller, J. (1999). Employ AI methods to control the behavior of animated interface agents. Applied Artificial Intelligence, 13(4-5), 415-448. Bull, S., Greer, J. and McCalla, G. (2003). The Caring Personal Agent. International Journal of Artificial Intelligence in Education, 13, 21-34. Razek, A. M., Frasson, C., and Kaltenbach, M. (2003). Context - based information agent for supporting intelligent distance learning environment. Proc. of the Twelfth International World Wide Web Conference. Agarwal, R., Deo, A. and Das, S. (2004). Intelligent agents in e-learning. ACM SIGSOFT software Engineering Notes, 29(2). Wooldridge, M. (2002). An Introduction to Multiagent Systems, chapter 16. West Sussex, England: John Wiley & Sons, Ltd.

Emotion & Personality

This page intentionally left blank

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

167

Research on Personality Mining System in E-Learning by Using Improved Association Rules Luo Qi1 2, Wu Yanwen1, Wan Liyong 1and Yu Ying1 Department of Information & Technology, Central China Normal University Wuhan, Hubei 430079, P.R.China 2 Department of Information Engineering ,Wuhan University of Science and Technology Zhongnan Branch Wuhan, Hubei 430223, P.R.China [email protected], [email protected] 1

Abstract: To meet the personalized needs of E- learning, an improved association mining rules was proposed in the paper. First, data cube from database was established. Then, frequent item-set that satisfies the minimum support on data cube was mined out. Furthermore, association rules of frequent item-set were generated. Finally, redundant association rules through the relative method in statistics were wiped off. The algorithm had two advantages, the first was that the execution time was short while searching for the frequent item-set; the second was that the precision of the rules was high. The algorithm was also used in personality mining system based on E-learning model (PMSEM).The results manifested that the algorithm was effective. Keywords: (-learning, Association rules, Personality Mining, Data cube

Introduction Nowadays, the importance of E-learning has transferred from how to solve the limit of space-time problem in traditional teaching to build up the personalized learning environment, and offer a kind of personalized knowledge service based on theories such as modern pedagogy, psychology, etc[1]. The learners are in different age level, sex, and social role, their culture ,education background, attention and interest are also exist a great difference. Giving corresponding learning content and tactics to realize teaching learners according to their needs is very difficult [2]. Its basic reason lies in being difficult by obtaining the relations between the learner's personality characteristics and learning behavior patterns accurately, automatically. In this way, it is necessary to mine out the association rules between personality characteristics and learning behavior patterns. The subsequent learners’ personality characteristics are deduced from their learning behaviors by using the rules above. Basing on it, personality learning model is set up and interesting groups are formed .Thus, personalized learning and cooperative learning are realized. At present, many scholars have carried on a great deal of researches on association algorithms, such as such as Apriori algorithm or improved Apriori algorithm FP-tree [3]. The execution time of their algorithms was long while searching for the frequent item-set and the rule’s interest degree was low. Even a large number of redundant rules are also included [4]. Besides, the complexity of space and time are high. According to this, an improved association rule mining algorithm is proposed in the paper. It has improved the traditional algorithm, and introduced data cube, relative method in statistics and adaptive adjusting mechanism in intelligence control [5]. The algorithm has two advantages, the first is that the execution time is short while searching for the frequent item-set; the second is that the precision of the rules is high. The algorithm is also used in personality mining system based on E-learning model (PMSEM).The results manifest that the algorithm is effective.

168

L. Qi et al. / Research on Personality Mining System in E-Learning

1. An Improved Association Rules The steps of an association rules mining based on data cube as follows: Step1: establishing data cube from database Step2: mining frequent item-set which satisfies the minimum support on data cube Step3: generating association rules of frequent item-set. Step4: redundant association rules are wiped off through the relative method. 1.1 Establishing data cube Supposed in personality model database, we observe the data by three dimensional angles. These three dimensions separately are learning behavior patterns dimension, learner's personality characteristics dimension, and time dimension. Then data cube is obtained through OLAP operation from data base [6]. Fig.1 is data cube by 3-D.

Fig. 1 Data cube by 3-D

1.2 Mining frequent item-set on data cube The paper has improved Apriori algorithm. The algorithm searching for the frequent item-set on data cube is called Personal_ Cube_Apriori algorithm. Lk Represents set of frequent k_ item-set, Ck Represents set of candidate k_ item-set The idea of adaptive adjusting of the Personal_ Cube_Apriori algorithm as follow: Firstly, the algorithm searches for frequent k_ item-set for each dimension. If some dimensions don’t have frequent k_ item-set, it shows that the dimension level is excessively low and we should drill through above and improve the dimension level. If in some dimensions, all frequent k_ item-set is frequent k_ item-set, it shows that the dimension level is excessively high and we should drill through under and lower dimension level. 1.3 Generating irredundant association rules After mining out frequent item-set, the process of generating association rules is composed of two steps˖ Step1: regarding to each frequent item-set l, all non- spatial subsets are generated. Step2: regarding to each non- spatial subset of frequent item-set l, if sup port _ count (l ) t min_ conf sup port _ count ( s )

(1)

Then the rule s=> (l-s) is generated. min_conf represent the minimum confidence thresholds, support count(l) represents the number of transaction containing item-set l, support count(s) represents the number of transaction containing item-set s. Lower interest degree rules are obtained in this way, so the redundant rules must be wiped off. Thus, the paper introduces the relative analysis method in statistics to wipe off the redundant rules. After mining out one association rule, we calculate the correlation among frequent item-set. Concept of correlation is formula 2

169

L. Qi et al. / Research on Personality Mining System in E-Learning

O(r ) E (r )

CORRr

(2)

CORRr More approaches to 1, the independence among set of r item-set is better. For example, the association rule ˴=>˵, X, Y are the item-set. If its correlation is worse, it must be the false strong rule. Relative analysis method is applied in k dimension data cube. If item or dimension Y is Y1 , Y2 , , Yr ̎values, X is X 1 , X 2 , X s s values, nij represents the number of X is Yi , and X is Xj. s

ni

¦n

ij

i 1, 2, , r

(3)

ij

j 1, 2, , s

(4)

ni n j

(5)

j 1

r

nj

¦n i 1

r

n

s

¦¦ n

ij

mij

n

i 1 j 1

F2

r

s

n¦¦ i 1 j 1

(nij  mij ) 2

(6)

mij

F 2 Is correlation degree among items, if all items are independent, F 2 is 0. Supposed

the critical value is assigned. If F 2 is bigger than the critical value, X and Y are statistical correlation. Otherwise, they are not statistical correlation. Namely, they are independent. 2. PMSEM The technologies of personality, data mining are applied in the paper [7], while the model of personality mining system based on E- learning (PMSEM) is shown in Fig.2

Fig. 2 The model of Personality mining system based on E-learning

The main function of PMSEM is to find out the association rules between personality characteristics and learning behavior patterns. The subsequent learners’ personality characteristics can deduce from their learning behavior by using the rules above.

170

L. Qi et al. / Research on Personality Mining System in E-Learning

3. Application of improved association rules algorithm in E-learning Based on above research, we combine with the cooperation item of personalized knowledge service system in network education .The author constructs a personality mining system website. The system is also applied in network institute of central normal university. The results manifest the system support personalized E-learning better the experiment is that we carry on the investigation and construct the database for personality attributes of 360 learners, 300 learners are selected to regard as the data source and other 60 learners data are selected to regard as the examination data. Step1: Data pretreatment and clean. First, not complete attributes values in database are filled in though rough theory. Then, according to the distribution of each attribute extreme value, sparser values is removed by minimum support min_sup =30%. Date converses to Boolean attributes according to the actual value scope of each attribute .thus, 100 attributes are obtained. Step2: Generating data cube. Data cube is generated through OLAP operation. These are three dimensions such as learning behavior patterns dimension, learner's personality characteristics dimension, and time dimension. Step3: Generating association rules. The frequent item-set is searched out though Personality_ Cube_Apriori. Then, F 2 is calculated and some false strong rules are removed. As a result of the application environment limited, the partial learning behavior patterns dimension and learner's personality characteristics dimension are carried on analysis though improved association rule algorithm. Learning behavior patterns selects two aspects such as learning courseware and issue information on BBS. 9766 multi-dimensional association rules are obtained. 6794 association rules are obtained though F 2 ˘166.7. There are 10 former and back integrity rules of them. Step4: Inputting the following learners’ behavior matches above rules, so learning characteristics is obtained. On the basis, best learning content and learning strategy are provided to them. So, personalized learning and cooperative learning are realized Step5: 60 Learners’ data is regarded as the examination data. Compared with above data, the results manifest that the system is effective. 4. Conclusion In summary, an improved association algorithm is proposed in the paper. The results manifest that the algorithm is effective through above algorithm performance research. Meanwhile, the model of personality mining system is proposed. The results manifest that the system mines out the association rules between personality characteristics and learning behavior patterns. The subsequent learners’ personality characteristics can deduced accurately, automatically from their learning behavior by using the rules above. References [1] Wu Yanwen, Luo Qi, “Research on Personalized Knowledge Service System in Community E-Learning”. Edutainment 2006 Proceedings. Lecture Notes in Computer Science, Volume 3942, 2006.4, pp.115-152. [2] Liu Jun, Li Renhou and Zheng Qinhua, “Study on the Personality Mining Method for Learners in Network Learning”. Journal of Xian Jiaotong University, 2004, 38(6), pp.575-576. [3] Han J and Kamber M, “Data Mining Concept and Techniques”. Academic Prints, 2001, pp.100-103. [4] Lu Lina and Chen Yaping, “Research on algorithm Apriori of mining association rules”. MINI-MICRO SYSTEM, vol 21, 2001.9, pp.942-944. [5] Yuan Wei, "data mining center of Statistics department of china renmin univer-sity",Statistics and information forum, 2002( 1),pp.5-9. [6] Yan Shizhuan and Li Zhanhuai, "Commercial Decision System Based on Data Ware-house and OLAP", Microelectronics & Computer, 2006(2), pp.66-67. [7] Luo Qi and Xue Qiang, “Research on Application of Association Rule Mining Algorithm in Learning Community”, CAAI-11, Wuhan, 2005, pp.1458-1462.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

171

Analysis on Relationships of Emotional Transmissions between Participants and Their Emotional Aspects in Communication Using Bulletin Board System Shogo KATO a, Yuuki KATO b, Kanji AKAHORI c Faculty of Human Sciences, Waseda University, Japan b School of Social Welfare, Tokyo University of Social Welfare, Japan c The Center for R & D of Educational Technology, Tokyo Institute of Technology, Japan [email protected] a

Abstract: This paper focuses on communication using bulletin board system (BBS). A practical experiment was conducted to investigate the degree of influence emotional transmissions between senders (writers) of replies and receivers (readers) of replies have on the emotions which they experienced during communication using the BBS. The analysis focused on the readers of postings. Twelve participants of this experiment were divided into two groups based on the degrees of emotional transmissions: a High group and a Low group. The emotions experienced during communication using the BBS were compared between the High group and the Low group. The results of this experiment showed a tendency for negative emotions such as anger and anxiety to increase when emotional transmissions are low (i.e., the Low group). Keywords: emotional transmissions, computer-mediated communication, BBS

1. Introduction Until relatively recently, interaction has been conducted entirely FtFC, or at the least synchronously. It is, therefore, unsurprising that in such contexts, we are highly effective in judging people’s characteristics, such as familiarity, gender, emotion or temperament (e.g., Cheng, et al., 2001). Judgment of other’s psychological states is a significant aspect of human interpersonal communication. It refers to the interpersonal process by which people employ all available information and make general judgments. Recent FtFC researches (e.g., Krauss & Fussell, 1996) have consistently indicated that both nonverbal and verbal cues jointly affect the process of judgment (Patterson, 1994). Technology now mediates much communication. Phone, e-mail or video-conference: in each case, people must make do with limited cues to help them estimate other people’s emotional states, dispositions and personalities. This is especially so when Computer-mediated communication (CMC) users cannot see each other and the CMC environment is restricted in terms of nonverbal cues. This has resulted in studies conducted to investigate judgment in CMC contexts (e.g., Kato et al., 2001; Markey & Wells, 2002). In particular, Kato, et al. (2001) have focused on judgment of other’s emotional states in e-mail communications, and found gaps between the sender’s self-report of emotional states and the receiver’s judgment of the sender’s emotional states. Their findings have showed that the judgment of emotions in e-mail communication lacks accuracy, and there is a tendency to misjudge the partners’ negative emotions to be hostile emotions in e-mail communication. However, there is little research

172

S. Kato et al. / Analysis on Relationships of Emotional Transmissions

on relationships between emotions experienced by senders and receivers and their transmissions of emotions. 2. Objectives We performed a practical experiment in the form of a simulated argument among university students while communicating using the BBS, within a university classroom lesson. In this research, the students' emotional aspect was measured in the discussion and the following an analysis was performed: Analysis focusing on the readers of postings. They (readers) read the reply to their postings and interpreted the emotions of the writer of the reply. This action was analyzed. From the analysis, we then considered the relationships of emotional transmissions between participants and their emotional aspects in BBS communication. 3. Method The participants consisted of twelve students from an all woman university in Japan (4 forth graders and 8 third graders) who enrolled in the "Human Media" subject. This practical experiment was carried out within the subject "Human Media" which the participants enrolled for in December, 2005. The subject focused on the design of Web pages and concentrated on design methods for high usability. This subject was a half-yearly subject that consisted of lectures (a total of 12 times) and an exercise Students were organized into groups of 2 or 3 persons. In their groups, the students considered the aspect of usability in the second half of each lesson. Starting from the eighth class, every group worked on the task of designing Web pages with high usability. In the last class, discussions were conducted on the BBS. Each participant perused the Web page designs created by other groups and argued on the best design to be used on the BBS. The time allocated for this task was 1 hour, and a tree view type BBS was used. The participants discussed on the BBS using aliases and throughout the practice of BBS, they were asked to avoid talking FtF. The participants were asked to complete the following four kinds of questionnaires during the discussion using the BBS. These four questionnaires asked about twelve kinds of emotions based on Izard, et al. (1993) (Interest, Joy, Surprise, Sadness, Anger, Disgust, Contempt, Anxiety, Guilt, Shyness, Inward Hostility, and Willingness) (Kato, et al., 2001). Two of these questionnaires (Emotional Interpretations and Emotional States at Receiving) measure the emotional aspects on the side of the receivers, and the other two questionnaires (Emotional Expectations and Emotional States at Sending) measure the emotional aspects on the side of the senders. The questionnaires consisted of twelve items, one item for each of the twelve emotions. In this paper, the analysis focused on two kinds of questionnaires, Emotional Interpretation and Emotional States at sending. Measurement of Emotional Interpretations: During the discussion using BBS, whenever the participants received a reply from the other students, they had to answer a series of questions using a five-point scale (1=not at all true, 5=very true). This was used as the index of ‘emotional interpretations’ to examine how each participant interpreted their partners’ emotions using the received reply. Measurement of Emotional States at Sending: Whenever the participants sent a posting to the other students, they also had to answer a questionnaire using a five-point scale (1=not at all true, 5=very true) as the index of ‘emotional states’ at sending, to examine how each participant felt when composing and sending the posting. The participants were required to reply to the above-mentioned questionnaires in the following two situations. (1) Once a participant replied to postings displayed on the BBS, she answered the questionnaires on Emotional Expectations and Emotional States at Sending, respectively. (2) Once there was a new reply from the other participants to the participant's posting, and after reading the reply, she then answered the questionnaires on

S. Kato et al. / Analysis on Relationships of Emotional Transmissions

173

Emotional States at Receiving and Emotional Interpretation, respectively. In other words, the participants answered the questionnaires after they replied the postings of others and after receiving replies from others. 4. Results and Discussion 4.1 Procedure of analysis The method of analysis performed in this research is described as follows. This research focused on emotional interpretations as the means to grade the degree of emotional transmissions between participants. It examined the degree the writer of posting’s emotion could be interpreted when reading the posting on a BBS. Kato, et al. (2006) was used as the index to grade these two emotional transmissions. The correlation coefficient between the emotions of 12 ‘emotional states at sending’ (which the writer of the reply answered at the time of posting) and the emotions of 12 corresponding ‘emotional interpretations’ was calculated to analyze the Emotional Interpretations. Then, based on the value of the correlation coefficient, the relation was classified into heights and converted into grades of ‘emotional transmissions’. Consequently, the relationships between the grades of emotional transmissions and emotional aspects were analyzed in the following manner. With regards to the height of emotional transmissions within emotional interpretations, a comparison was made between the height of emotional transmissions, and the height of emotional interpretations, respectively. In addition, analysis of the questionnaires data resulted in the classification of the 12 feelings into positive emotions and negative emotions based on the factor analysis of Kato, et al. (2005). "Interest", "joy", "surprise", and "willingness" were classified into positive emotions, and the rest of the emotions were classified as negative emotions. A t-test was conducted to compare using the values which averaged the consultation values of the emotions which corresponds, respectively. The following explains the basic data such as the number of postings in the BBS. In total, the participants posted a number of 126 postings. 61 postings were replies to other postings. In addition, the combination of the emotional interpretations and emotional states at sending was 38. Therefore, the correlation coefficient of 38 was calculated on each relationship. In this research, the correlation coefficient of 0.80 or more is considered as high emotional transmissions and 0.65 or less is considered as low emotional transmissions. Consequently, in the emotional interpretations, the combination of 13 was in each height. 4.2 Analysis on receivers’ aspects The data collected was analyzed to answer this question - What kinds of emotions (under which a writer write postings) would enable the reader to more correctly interpret the writer’s emotion? The heights of the emotional transmissions based on emotional interpretations were compared using the data obtained from the questionnaire of emotional transmissions. Each of the positive emotions and negative emotions was compared using t-test. The result is shown in Table 1. With regards to positive emotions, a significant difference was not seen between heights. On the other hand, with regards to negative emotions, a significant difference (t(24)=2.16, p<0.05) was seen between heights. In low emotional transmissions, negative emotions were highly produced as shown by Table 1. Then, we investigated whether the reader was able to accurately interpret the writer’s emotions. The heights of the emotional transmissions based on emotional interpretations were compared using the data obtained from the questionnaire of emotional interpretations. Concerning each of the positive emotions and negative emotions, a comparison was made using t-test. The result is shown in Table 1. With regards to positive emotions, the

174

S. Kato et al. / Analysis on Relationships of Emotional Transmissions

significant difference (t(24)=2.11, p<0.05) was seen between heights. On the other hand, with regards to negative emotions, no significant difference was seen between heights. In high emotional transmissions, positive emotions were highly produced from Table 1. Table 1 Comparison of emotions in the High and Low groups t test, *p<0.05 Emotional states at sending Emotional interpretations Positive Negative Positive Negative High 2.98 1.02 * High 3.19 * 1.08 Low 2.71 1.16 Low 2.67 1.10 4.3 Discussion This paper considers the ‘emotional interpretation’ as ‘emotional transmissions’ on the BBS. The results showed that there was a tendency for inaccurate interpretation of emotions to be made when a writer posted under more negative emotions. In contrast, when a writer posted under more positive emotions, there was a tendency for more accurate interpretation of emotional to be made. The result of the experiment on e-mail communications (Kato, Kato & Akahori, 2006) showed similar tendency as that of the tendency of this research. The authors believe that a clearer result was not visible in this practice since there was almost no specific evocation of negative emotions. However, whilst referring and considering the knowledge of previous researches, it is thought that it is easier to inaccurately interpret and expect negative emotions than positive emotions. Therefore, in the case of communication using a BBS, it is thought that a participant's emotion is important. The implication from the result of this research is that misunderstanding between participants can be reduced when participants write postings and replies with more positive emotions. The following explains the limitations of this research and future work. It is necessary to analyze writer’s aspects, the relationship between emotional states at receiving and emotional expectations. In addition, as all the participants in the practice of this research were female, the authors believe that from now onwards, it is necessary to examine the results on male participants and to analyze gender differences. Moreover, the analysis performed in this paper did not examine the contents of postings. As the sentences within the postings are the medium of emotional transmissions, from now on, it is important to conduct a detailed analysis of the contents of the postings. In addition, this analysis should also include the examination on the flow of postings. Finally, we would like to express our sincere thanks to Ms. Safiza Markhayu Yusof for her assistance. References [1] Cheng, Y., O’Toole, A., & Abdi, H. (2001). Classifying adults’ and children’s faces by sex: Computational investigations of subcategorial feature encoding. Cognitive Science, 25, 819-838. [2] Izard, C.E., Libero, D.Z., Putnam, P., & Haynes, O.M. (1993). Stability of emotion experiences and their relations to traits of personality, Journal of Personality and Social Psychology, 64, 847-860. [3] Kato, Y., Kato, S., & Akahori, K. (2006). Effects of emotional cues transmitted in e-mail communication on the emotions experienced by senders and receivers. Computers in Human Behavior, (In Press) [4] Kato, Y., Sugimura, K., & Akahori, K. (2001). An affective aspect of computer-mediated Communication: analysis of communications by e-mail. Proceedings of ICCE/SchoolNet 2001, 636-642. [5] Krauss, R. M., & Fussell, S. R. (1996). Social Psychological models of interpersonal communication. In E. T. Higgins, & A. W. Kruglanski (Eds.), Social Psychology: Handbook of Basic Principles (pp. 655-701), NY: The Guilford Press. [6] Markey, P., & Wells, S. (2002). Interpersonal perception in internet chat rooms. Journal of Research in Personality, 36, 134-146. [7] Patterson, M. L. (1994). Strategic functions of nonverbal exchange. In J. A. Daly & J. M. Wiemann (Eds.), Strategic Interpersonal Communication (pp. 273-293), Hillsdale, NJ: Erlbaum.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

175

Learning Nonverbal Emotion Interaction In 3D Intelligent Virtual Environment For Children Zhen Liua Faculty of Information Science and Technology, Ningbo University, 315211, China [email protected]

a

Abstract: Nonverbal emotion interaction is very important for children, learning nonverbal emotion interaction by computer technology is an interesting subject. In a certain intelligent virtual environment (IVE), multi virtual characters interact with emotion and construct an virtual society. Modeling nonverbal social interaction is very important for constructing believable virtual characters. Nonverbal social interaction is a direct communication manner for virtual characters in an IVE. A cognitive architecture of virtual character is presented in this paper, the architecture outlines high-level knowledge of virtual characters in IVE, some new concepts on nonverbal social interaction is set up. A virtual character has the ability of social interaction with other virtual characters by cognitive architecture. Keywords: Nonverbal, emotion interaction, intelligent virtual environment

Introduction Children’s development is very important for modern society, there are nearly 400 million children in china, but each family has only one child. Chinese children get a lot of care from their parents, however, some children lack emotion expression for their parents, and much children lack the experience of emotion interaction with others. In recent years, children’s crime is on the rise, people have already realized that affective education has already become the indispensable contents for children. A child should has ability of emotion communication with other is an important aspect of affective education. Emotion communication is expressed by verbal and nonverbal manner. In fact, nonverbal communications are even more important than verbal information, particular in respect of emotion interaction. Nonverbal communication includes a variety of signals, body language, gestures, tough, physical distance, facial expression, and nonverbal vocalization [1]. Human beings need nonverbal emotion interaction from infancy to old age, and nonverbal emotion interaction is so vital to children that without it their emotional development will be stunted. A child should master some basic manner of nonverbal emotion interaction, and he should learn how to watch emotion meaning from other’s facial expression or body language. In general, a child will spend a lot of time on learning nonverbal emotion interaction in real life, the purpose of the paper will support a new method of learning nonverbal emotion interaction for children. In recent years, virtual reality and artificial life are blended each other, intelligent virtual environment (IVE), a new research field is born in the intersection between virtual reality and artificial life. In an IVE, there are a lot of virtual characters. In order to realize believable intelligent virtual characters, we can use cognitive model with emotion mechanism to control the artificial

176

Z. Liu / Learning Nonverbal Emotion Interaction in 3D Intelligent Virtual Environment

behavior of virtual characters. We can construct a vivid virtual world where some virtual characters interact each other with manner of nonverbal emotion interaction. Social interaction of virtual characters is an interesting research subject in intelligent virtual environment (IVE)[2][3][4], a believable virtual character has the ability of social interaction to other virtual characters with verbal and nonverbal manner. There are a lot of researches on nonverbal behavior expression of virtual characters, but little on how interaction triggers and ends when two virtual character en-counters with social status. For example, Thalman presented the concept of virtual society [5]. He proposed to use agent architecture to build such virtual society. Cassell et.al developed behavior expression toolkit for virtual characters with XML-based language [6], Pelachaud created a subtle method of facial expression for virtual characters [7], Chi et.all built a system called Emote to express natural movements of posture virtual character [8]. Unuma presented a method of creating emotion-based locomotion [9]. Rose used motion-blending technique to product emotional walking [10]. Based on these researches and author’s previous work [11]-[14], the paper present a new method to construct virtual characters that can interact each other with social status. A cognitive architecture of virtual character is presented in this paper, a virtual character is provided with a cognitive architecture to control nonverbal emotion interaction, and a demo system is realized on PC. 1. Cognitive Architecture of Virtual Characters Nonverbal social interaction is crucial for behaviors of virtual characters in a virtual society. The section is based on cognitive modeling [3]. A virtual character is regarded as an agent with a built-in cognitive architecture that can control behavior and action. On the basis of cognitive modeling, a virtual character can be equipped with high-level knowledge about virtual world, and he can collect information, express emotion, make navigation and move to goal. A cognitive architecture includes four parts as follows: (1) The first part can sense and perceive virtual environment’s information from memory. A virtual character should be equipped with visual, tactile and auditory sensors in order to execute the perception. Synthetic vision is an important method for visual perception [5], which can accurately simulate the vision from view of character, and we realized the method synthesis vision on PC. Synthetic vision will be costly. Furthermore, this method cannot get the detail semantic information of objects. Therefore, we present another efficient method for simulation of visual perception. Based on the Gibson’s theory of affordances[15], an affordance is invariance for environment. A character can perceive these affordances directly. In this paper, we can use the Gibson’s theory to guide navigation, affordances of objects hints navigation information. (2) The second part can make decision by information, it include plan module, behavior module, social norm, mental module. Plan module execute behavior plans by a set of productive rulers. Behavior module creates behavior codes by behavior plans codes. Inhibitory gain and fatigue are time sequence characteristic of behavior. The higher Inhibitory gain, the longer the duration of the behavior is and new behavior is excited only under new stimulus. Fatigue means that behavior with low degree of priority can obtain the chance to carry out, once a certain behavior is carried out, the behavior will stop at some time. We introduce the inhibitory gain coefficient (a real number greater than one) and fatigue coefficient (a real number smaller than one) to measure inhibitory gain and fatigue correspondingly. Social norm module includes status, interaction information and interaction rules, it controls the process of a nonverbal social interaction, it provides the social knowledge for virtual character. Mental module includes emotion, personality and motivation. Based on emotion OCC model [16], we can use a computational model of emotion [13]. Emotion module read external stimulus from memory (the perception module

Z. Liu / Learning Nonverbal Emotion Interaction in 3D Intelligent Virtual Environment

177

write stimulus information to memory module). Activation of an emotion is relative to external stimulus and inner mental variables. If an emotion is active, this module will create emotion expression, emotion expression code will be sent to behavior module. Emotion is core of the module, and personality is some stable psychological traits of a virtual character, and motivation variables include some physiology parameters of a virtual character. (3) The third part executes the behaviors through body posture and facial expression. We can use inverse kinematic arithmetic and motion blending to drive nonverbal emotion posture, and we can use geometry blending to create facial expression. (4) The four part includes database and knowledge. Database includes 3D geometry of virtual environment, original information, such as, the original location and parameters of virtual character, motion capture data, 3D model and location of objects, default motion plan scripts that record some goal location. Memory can serves as a center of information share among all other modules. Knowledge module includes guiding knowledge in environment for virtual characters. For example, the meanings of objects in environment are part of knowledge. 2. A Demo System of Virtual Character This paper uses inverse kinematics to simulate the walking animation of a virtual character, some high-level parameters (walking step, goal point, etc.) can control a walking process, and so a virtual character can walk to anywhere by outer stimulus. In order to enhance the reality of walking, some emotional motion capture clips can be blended with waking clips to create an emotional walking. We can use synthesis model of expression method to create infinite facial expressions.

happy Figure. 1

sad

happy and sad

virtual character has complex facial expression

Figure. 2 Nonverbal emotion interaction between two characters 3. Conclusion and future work Intelligent virtual environment (IVE) will play an important role in the affective education

178

Z. Liu / Learning Nonverbal Emotion Interaction in 3D Intelligent Virtual Environment

of children. In a certain IVE, multi virtual characters interact with nonverbal behaviors and construct a virtual society. This paper presents a cognitive architecture of social interaction for virtual characters. Social norm is an important component in cognitive architecture. A social norm includes status information, interaction signals and interaction rules. A demo system is realized on PC. Simulation of social interaction for virtual characters is a very difficult subject. This paper only gives a primary outline for interaction model in this paper. In fact, social interaction is related to many factors, such as different culture, emotion, personality, motivation etc. We can use the above demo system to teach children to learn nonverbal emotion interaction.

Acknowledgments The work described in this paper is co-supported by science and technology project of Zhejiang Province Science Department (grant no: 2006C33046), University Research Project of Zhejiang Province Education Department (grant no: 20051731), forepart professional research of ministry of science and technology of the People's Republic of China (grant no: 2005CCA04400), References [1] Zastrow. C, Kirst-Ashman. K.(1987) Understanding Human Behavior and the Social Environment. Nelson-Hall Publisher press, Chicago, 322-323. [2] Tu, X., Terzopoulos, D.(1994) Artificial fishes: Physics, locomotion, perception, behavior. Proceedings of SIGGRAPH’1994, ACM Press, 43-50. [3] Funge, J., Tu, X., Terzopoulos, D.(1999) Cognitive Modeling: Knowledge, Reasoning and Planning for Intelligent Characters. Proceedings of SIGGRAPH'99, ACM Press, 29-38 [4] Badler. N., Phillips. C., Webber. B.(1993) Simulating Humans: Computer Graphics Animation and Control. Oxford University Press, New York, 154-159. [5] Magnenat-Thalmann. N., Thalmann. D. (eds)(2004) Handbook of Virtual Humans. John Wiley & Sons. [6] Cassell, J., Vilhjalmsson, H.H., Bickmore, T.(2001) BEAT: the behavior expression animation toolkit. Proceedings of SIGGRAPH’2001, ACM Press, 477–486. [7] Pelachaud, C., Poggi, I.(2002) Subtleties of facial expressions in embodied characters. Journal of Visualization and Computer Animation. 13, 287–300. [8] Chi, D., Costa, M., Zhao, L., Badler, N.(2000) The emote model for emote and shape. Proceedings of SIGGRAPH’2000, ACM Press, 173–182. [9] Unuma. M., Anjyo. K., Takeuchi. R.(1995) Fourier Principles for Emotion-Based Human Figure Animation. Proceedings of SIGGRAPH'1995, ACM Press , 91-96. [10] Rose .C. F., Cohen. M., Bodenheimer. B.(1998) Verbs and Adverbs: Multidimensional Motion Interpolation. IEEE Computer Graphics&Application, 18, 5, 32-40. [11] Liu .Z.(2002) Emotional Behavior Animation of Virtual Human In Dynamic Virtual Environment. Proceedings of the 8th Joint International Computer Conference, Zhejiang University Press, 190-193. [12] Liu .Z, Pan. Z.G., Zhang. M.M.(2002) Behavior Animation of Virtual Human In Virtual Society. Proceedings of the twelfth International Conference on Artificial Reality and Telexistence(ICAT2002), 186-187. [13] Liu Z, Pan, Z.G.(2005) An Emotion Model of 3D Virtual Characters In Intelligent Virtual Environment. Proceedings of the first international conference on affective computing and intelligent interaction, ACII2005, LNCS 3784, Beijing, China, 629-636. [14] Liu.Z.(2006) a synthesis of emotion in emotion vector space, proceedings of 2006 international conference on artificial intelligence, Publishing House Bupt, 45-48. [15] Gibson, J.J.(1986) The ecological approach to visual perception. Lawrence Erlbaum Associates, Hillsdale, New Jersey. [16] Ortony, A., Clore, G.L., Collins, A.(1988) The cognitive structure of emotions. Cambridge University Press, New York.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

179

Development of Know-how Information Sharing System in Care Planning Processes Kaoru ETOa, Tatsunori MATSUIb, Yasuo KABASAWAa a Faculty of Engineering, Nippon Institute of Technology, Japan b Faculty of Human Sciences, Waseda Universty, Japan [email protected]

Abstract. The purpose of this study is to develop a computer support system for educating personnel who are involved in care management. We propose a system in which know-how information can be shared. We consider that visualizing and showing care plans drawn up by experts in various forms allows a beginner to see the differences between a beginner’s plan and expert plans. Sharing know-how information is possible by recording, accumulating, and giving titles to what has been noticed in comparing documents. This system can visualize the similarities among documents that interpreted the results of an assessment, and can flexibly change different viewpoints, additionally, can map a user's new document into a two-dimensional document space. We confirmed that these functions were appropriate.

Keywords: know-how information sharing, changing viewpoint, concept-base

Introduction In order to improve the quality of care, it is necessary that we establish a method of care manager training. There are four important processes in care management. This system supports the process in which the result of an assessment is interpreted. We decided to assist a beginner in noticing weaknesses in her care plan by visualizing and showing care plans drawn up by expert in various forms. Differences between a beginner's plan and an expert's appear most typically in their different viewpoints to interpret the results of an assessment. Making these differences noticeable is one method of extracting know-how information. In order to promote user awareness, we visualized similarities among a user's documents and an expert's documents, and achieved a function of flexibly changing viewpoints. We also confirmed that these results were appropriate.

180

K. Eto et al. / Development of Know-How Information Sharing System

1. The Method and Support Functions of Know-how Information Extraction We consider the support functions in which a viewpoint can be flexibly changed and the results can be visualized. We extract know-how information using these functions. This idea is shown in Fig.1. (1) A user refers to the statistic values of an original idea or a KOMI (Kanai Original Modern Instrument) chart, "graphical recording sheet" and then changes the viewpoints. (2) Based on these viewpoint changes, the user moves vertically and horizontally in the hierarchy of a concept-base, and calculates the degree of similarity each time. (3) The system then classifies the document from the calculation results. (4) Next, the system visualizes the results in a two-dimensional document space displayed on the computer. (5) By clicking on the document number according to its classification, the KOMI chart that originates the document is shown. (6) Finally, the user records, accumulates, and gives a title to what has been noticed.

Moving right and left 㧔view relationship㧕

M ovement on the hierarchy of the concept-base

Calculation of degree of similarity

M oving up(expansion) M oving down(focus on)

Change of viewpoint

User

Specific Knowledge recording

Sharing know-how Giving a title

Extracting know-how

Specific Knowledge

General Knowledge Separation of Specific Knowledge and General Knowledge

Awareness of difference

Change of grain size of cell on the chart

X

X

㧞 Y ԙ 㧝 㧟 Ԙ Ԛ 㧡 㧢

ԝ

Y 㧞 ԙ 㧝Ԙ 㧠 Ԙ ԛ 㧣 㧟 Ԛ Ԟ Y 㧡 Ԝ 㧞㧟 ԙ

X

㧠 ԛ

Ԛ

㧢 ԝ

Accumulation of title

Fig.1.The model for extracting and sharing the know-how information

2. A Concept-base and Similarities between Documents We constructed a concept-base that has a tree structure with six levels. Viewpoints are changed by moving up and down the levels of this concept-base. The concept-base contains about 4300 terms that consist of keywords extracted from the documents that include the KOMI chart, the assessment items, and the textbook for the KOMI chart. The extracted keywords were encoded based on a Japanese language thesaurus. The code is used to identify the position on the hierarchy of the concept-base. The distance between keywords is determined by the nearness of the concept between keywords. The distance between keywords is actually defined as the sum total of the weight of the branch of the node which reaches other keywords from one keyword. The distance between documents is determined by the distance between the keyword sets [1].

181

K. Eto et al. / Development of Know-How Information Sharing System

3. Mapping the Document of a New Plan into Two-dimensions

Change viewpoint Display result

this function to highlight the effects that promoted an awareness of the differences between a user and an expert. We wish to determine the two-dimension coordinate for a new case.

Making new Read new care plan document

Expert's documents mapped into two-dimension space by Kruskal's [2] method previously. Beginner's new document map in same space. We expected

C are D esigner Extract docum ent M orphological analysis C alculate im portance of keyword C alculate sim ilarity E xtract three near cases C alculate coordinate D isplay on tow-dimension D isplay chart

C are D esigner ’s D atabase Radar C hart K O M I C hart Sum m ary C hart D ocum ents Concept-B ase M odel cases D atabase D ata of keyword set classified by field Coordinate D ata

The step is shown in Fig. 2. Fig.2 Procedure for mapping new case (1) Make new care plan using “Care-designer”, into Two-dimensions which we have developed the software for making a care plan [3]. (2) (3) (4) (5) (6) (7)

Extract document from the database of “Care-designer”. Analyze the document with software of morphological analysis [4]. Extract keywords from the result of analysis [5]. Calculate the distance between expert' documents and beginner's new document. Choose three cases that is the nearest to the new case. Determine the coordinate of new case by distances and coordinates of three cases.

4. Discussion The left of Fig.3 shows the results of having added the new case,”No.36”. Three cases that were the nearest to the new case from the similarity calculation results were No.33, 4, and 29. No.36 was not mapped on the position that was necessarily nearest to the three examples. However, the new case was mapped into an appropriate range. This result was based on the properties of the multidimensional scaling, by increasing the number of cases, the similarity calculation results and the results of mapping into two-dimensions became closer. Two cases, "No.1, No.2" were near to the new case. The clients in three cases have a common feature that suffered from dementia, cerebral palsy, etc. The results of analyzing the keywords are as follows: (1) The same keywords found were found on two occasions in case 1, and on five occasions in case 2; and (2) Keywords sharing a sibling relationship were found on four occasions in case 1 and on five occasions in case 2. In chart of Fig. 3, four items are the same result of assessment in the left chart, 49 items are the same in the center chart and 53 items are the same in the right chart. From these results, we concluded that two-dimensional mapping was appropriate.

182

K. Eto et al. / Development of Know-How Information Sharing System

Fig.3. Mapping into two-dimensions added the new case and displaying of the chart of cases near to a new case 5. Conclusion We have developed the system to make a beginner notice the difference between beginner's care plan and an expert's by visualizing a care plan with various forms that were drawn up by an expert. As a function, the similarities among documents that interpret the results of an assessment were calculated using a concept-base, and the results were then mapped into two-dimensions. In order to promote user awareness, we created a function that shows the position of the user's new plan in relation to an expert's plan group in a two-dimensional document space. We confirmed that the results of mapping these documents into two-dimensions were appropriate. We consider that these support functions make a beginner notice the difference between a beginner's viewpoint and an expert's viewpoint, and that know-how information can be extracted. As a future work, we will experiment to evaluate the effectiveness of the system in a hospital and nurses' college.

References [1] Eto K., Matsui T., Kabasawa Y. (2006) Development of Know-how Information Sharing System in Care Planning Processes – Mapping New Care Plan into Two-Dimensional Document Space, Proc. of KES’2006, Part2, pp.977-984. [2] Okada A.,Imaizumi T. (1994) Multidimensional scaling using Personal Computer Kyoritsu Shuppan, Tokyo (in Japanese). [3] Eto K., Matsuda I. (2002) Development of a Care Plan Tool Using Assessment Information Visualization, Trans. IEICE, Vol. J85-D-I, No. 7, pp. 701-710 (in Japanese). [4] http://mecab.sourceforge.jp/. [5] http://gensen.dl.itc.u-tokyo.ac.jp/.

CSCL

This page intentionally left blank

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

185

Student Learning and Team Formation in a Structured CSCL Environment Nobel Khandaker, Leen-Kiat Soh and Hong Jiang Computer Science and Engineering University of Nebraska-Lincoln 256 Avery Hall, Lincoln, NE 68588-0115 {knobel, lksoh, jiang}@cse.unl.edu Abstract. A computer-supported collaborative learning (CSCL) environment aims to facilitate student learning by letting them work in small teams with the support of computer technology. Two important factors that affect work in such scenario are: interaction among the students and compatibility or interactivity of the team members. I-MINDS is a tool designed to support structured Computer Supported Collaborative Learning. In I-MINDS we try to improve both the quality of student learning and the quality of student team work with the help of intelligent agents. I-MINDS facilitates student interaction through its forums and can build student teams. In our two-semester long experiment we studied the effect of the interaction environment on the learning and performance of students in face-to-face and structured CSCL scenarios. Moreover, we studied how a student’s self-reported efficacy, teamwork and interaction skills affect his or her performance in face-to-face and structured CSCL settings. Our results indicate that even though students prefer face-to-face interactions; structured CSCL environment may increase their individual performance. Furthermore, we find that factors such as the difficulty of the problem or task to be solved and team member compatibility influence the quality of teamwork in structured CSCL. Keywords: CSCL, Jigsaw, Collaboration, Team, Learning

Introduction Computer Supported Collaborative Learning (CSCL) has been studied as a tool to increase student learning [14, 4, 9]. In a CSCL environment, students work together in small teams to solve complex problems. In structured collaborative learning, students are guided within a set of prescribed activities so that each activity has a set goal and measurable outcomes. The long term goal of our research is to support structured collaborative learning using an intelligent multiagent system where students and instructors may or may not have face-to-face interactions. In a typical CSCL environment, students learn from each other by interacting with their team members and by teaching their team members [5]. However, not all student teams work well [4, 8]. In some cases the students work in teams rather than working as a team [4, 8, 11]. To work together as a team well, the students need to build a shared understanding of what they are working on as well as how they are working together [11]. Even though the team formation procedure has been investigated by researchers [6, 7, 13]— such as handling information flow during team formation and transitioning between phases of team formation, the relationship between a student’s performance and his or her teamwork activities within a CSCL environment has not been addressed. As a CSCL tool, I-MINDS [10, 14, 15, 16] provides an environment where student participants are able to interact with each other through text messaging. That is, students interact without face-to-face interactions in the same room. Furthermore, I-MINDS can

186

N. Khandaker et al. / Student Learning and Team Formation in a Structured CSCL Environment

automatically form student teams. The team formation algorithm in I-MINDS works by allowing students to form teams based on the performance and compatibility of the participating students. In this paper, we discuss the results of our two-semester long experiment of deploying I-MINDS in an introductory CS course. Our experiment was conducted by dividing the students in control and treatment sections. The students in the control section participated in structured collaborative learning using conventional face-to-face interaction. However, the students in the treatment section participated in structured computer-supported collaborative learning and interacted with each other through I-MINDS. During our experiment, we investigated the following issues: x How does the teamwork in conventional structured face-to-face collaborative learning compare to the teamwork in structured computer-supported collaborative learning? x How does the structured CSCL environment impact student learning or performance? x How do a student’s self-efficacy, perception of his or her peers, and perception of teamwork relate to his or her individual performance? Note that we have previously reported the structure of I-MINDS [17] and our implementation of Jigsaw collaborative learning model [1, 3]. Thus, in the following, we will only focus on the experiment and results, emphasizing on how students perceived teamwork and their peers, and the impact of I-MINDS on student learning and teamwork. 1. I-MINDS 1.1 I-MINDS Structure I-MINDS, which stands for Intelligent Multiagent Infrastructure for Distributed Systems in Education, consists of a set of intelligent agents. There are three types of agents: teacher agents, group agents (for supporting teams), and student agents. In I-MINDS, a teacher agent interacts mainly with the instructor. It helps the instructor deliver instructional content to the students and coordinates the team formation process in the virtual classroom. A teacher agent also helps the instructor answer student questions with the question ranking and question grouping modules. In addition, a teacher agent helps form student teams for structured collaborative learning, supporting the Jigsaw procedure. In an I-MINDS supported classroom, a student agent serves a unique student. It interacts with the student and exchanges information with the teacher agent and the group agents. It also maintains a dynamic profile of the student to whom it is assigned and a dynamic profile of the peers that the student has interacted with through I-MINDS. Finally, a group agent in I-MINDS is activated when there are structured collaborative learning activities. Structured collaborative learning involves specified activities that explicitly require students to cooperate. Currently, I-MINDS implements the Jigsaw model [1, 3]. The team activities monitored by the group agent include the number and type of messages sent among team members, self-reported teamwork capabilities, peer-based evaluations as a team member, and evaluation of each team. A detailed description of I-MINDS can be found in [14, 16]. 1.2 Team Formation in I-MINDS The teacher agent forms student teams using the VALCAM algorithm [15]. In VALCAM, the teacher agent acts as a coordinator for the coalition formation process and makes global decisions, such as, what should the least number of members in a coalition be, how long should the teams last, how the performance of each coalition should be evaluated, etc. The group agents then manage the teams. Each group agent monitors the performance and activities of the members of its assigned team. After the coalition has completed their tasks, the group agent also evaluates the performance of each student agent as a team member.

N. Khandaker et al. / Student Learning and Team Formation in a Structured CSCL Environment

187

The VALCAM algorithm is based on reinforcement learning of the student agents. In the beginning of every session the teacher agent chooses a few students and then initiates the team formation. During the team formation process, each student agent bids—an iterative Vickrey auction—to join its favorite team based on virtual currency earned from previous Jigsaw activities. Once the teams are formed, the group agents work with their team members to complete tasks assigned by the instructor. Finally, when the assigned task is completed, each student agent is rewarded with virtual currency based on its (student’s) performance as an individual and as a team member. Students who performed well will receive more virtual currency, allowing them to more successfully bid for their favorite teams in future sessions. Briefly, the VALCAM algorithm works as follows. Initially, the students or users are given some default amount of virtual currency to start with. In a typical coalition formation round, the auctioneer announces a task, and then the students post their self-efficacy in a blackboard. Then the auctioneer chooses the first members of the teams according to their current virtual currency balance. After the first members of the teams are chosen, the other members are assigned to each team by iterative auction. While bidding to join a team, a student agent considers how competent that team’s members think they are and what its past working experience with the members already in the team was. So, student agents who join a team first influence how the team takes shape. The number of members per team is the same or greater than the number of subtasks in the task given. Over time, as the students gain experience in their interactions with other students and participate in different tasks, students accumulate varying amounts of virtual currency: students who have been in a team that produces good results, or have performed well in the post-tests, or have been rated high by their peers will be rewarded more. As a result, the student agents of these students will be able to offer higher bids in order to join teams more likely as first members, and allow their students to form teams most compatible with them. So, the student agent in VALCAM incorporates the two important aspects of successful student collaboration: the competence of the members of the team it is trying to join and its interaction history with the members of the team it is trying to join. Details of this algorithm can be found in [14, 15]. 2. Experiment and Results 2.1 Experiment Setup To investigate the issues outlined in Introduction, a two-semester long study was carried out in the closed labs of CSCE 155 at the University of Nebraska-Lincoln, in Spring and Fall semesters of 2005. CSCE 155 is the first core course required of computer science and computer engineering majors. It has three 1-hour weekly lectures and one 2-hour weekly laboratory sessions. In each lab session, students were given specific lab activities to experiment with Java and practice hands-on to solve programming problems. For each semester, there were 2-3 lab sections where each section had about 15-25 students. The study utilized a control-treatment protocol. In the control section, students worked in Jigsaw collaborative learning teams without using I-MINDS. Students were allowed to move around in the room to join their Jigsaw teams (main group and focus groups [14]) to carry out face-to-face discussions. In the treatment section, students were told to stay at their computers and were only allowed to communicate via I-MINDS—with this setup, we essentially simulated a distance classroom environment. For each lab, the students were given a lab handout with a list of activities—thus, a lab is a task and its activities are the subtasks. The students of both the control and treatment sections were required to complete the tasks and subtasks in the four Jigsaw phases [14]. In each section, the instructor first announced the task and asked the students to fill

188

N. Khandaker et al. / Student Learning and Team Formation in a Structured CSCL Environment

out a Self Efficacy Questionnaire (SEQ) [14] to describe their competence in that area. Then the instructor announced the main groups. In the control section this was done by matching the strong student with the weak students (first goes with last and so on). In the treatment section, I-MINDS formed teams using the VALCAM algorithm. Once the main groups were formed, the teacher agent formed the focus groups by randomly selecting students from the main group. Then, every focus group was assigned one subtask randomly. After the subtask assignment, the focused exploration phase was started. Then the remaining Jigsaw Phases were carried out in order, during which the student agents and the group agents monitored and guided the activities of the students and the student teams, respectively. After all Jigsaw Phases were executed, all the students filled out the Peer Rating Questionnaire (PRQ) and Team-Based Efficacy Questionnaire (TEQ) [14] and took a 10-minute post-test. The post-test score was used as a measure of student individual performance in terms of understanding the lab topic. 2.2 Results First, we look at the average normalized post-test scores, as shown in Figure 1. Each normalized score is computed by dividing each student’s post-test score for a test day by the sum of the student’s post-test scores of all other lab days that did not involve the Jigsaw experiment. Therefore, this normalized score provides a measure to compare the performances of the control and treatment section students in a scale that does not depend on the individual student’s abilities.

Figure 1: Control vs. Treatment: Average normalized post-test scores for (a) Spring 2005, (b) Fall 2005

For both Fall 05 and Spring 05 experiments, one tailed t-test reveals that the average normalized post-test score of the treatment section is significantly higher than that of the control section ( D 5% , p value for Spring is 6 u10 4 and Fall is 5 u10 5 ). This indicates that I-MINDS-supported structured collaborative learning outperformed the conventional face-to-face one. This result also support those observed in [2, 12]. On average, students in the treatment section achieved better standard deviation—meaning that these students’ post-test scores were more tightly clustered than those of the control section (1.67 vs. 2.36 in Spring 2005, and 1.11 vs. 1.25 in Fall 2005). We also observe that students in the treatment sections seemed to improve over time, and their performance seemed to eventually overtake that of the control sections’—indicating that VALCAM, due to its learning mechanism, might have been effective in forming better and better teams over time. The Peer Rating Questionnaire (PRQ) surveys were conducted in both control and treatment sections after each lab session was completed. The PRQ is designed to quantify the compatibility of the team members after they have gone through the collaborative learning process. The average peer rating scores that each student gave to his or her team members can be used as a measurement of how well the team members were able to work with one another. Table 1 shows the results of the PRQ surveys. Students in the control

N. Khandaker et al. / Student Learning and Team Formation in a Structured CSCL Environment

189

section rated their peers better (higher means) and more consistently (lower standard deviation values) than those in the treatment section. This is possibly due to the convenience of face-to-face interaction since I-MINDS still lacks sufficient GUI features and multimedia capabilities to fully support real-time interactions. Spring 2005 Fall 2005 Control Section Treatment Section Control Section Treatment Section Mean Stdev. Mean Stdev. Mean Stdev. Mean Stdev. 1 42.10 2.73 32.45 5.78 35.39 2.30 33.71 4.69 2 36.62 7.05 37.72 4.60 34.87 5.32 35.80 12.21 3 39.91 4.80 34.63 8.08 36.03 3.19 36.37 5.18 4 N/A N/A N/A N/A 37.53 3.37 37.25 3.62 Mean 39.54 4.86 34.93 6.15 35.95 3.54 35.78 6.42 Table 1: Control vs. treatment sections: Results of the peer-rating questionnaires (PRQs) Session

On the other hand, there are indications that students in the treatment section for the Fall 2005 sections seemed to rate their peers better gradually as the semester progressed (from 33.71 to 35.80 to 36.37 and 37.25) and seemed to rate their peers more consistently as well. This might be due to the ability of the team formation algorithm in forming better teams over time, as indicated earlier. This evaluation in the form of PRQ then helped them choose better team members in the future sessions. The Team-Based Efficacy Questionnaire (TEQ) surveys were collected after each lab based on a set of questions designed to measure how a student viewed how well his or her team had performed, as shown in Table 2. Spring 2005 Fall 2005 Control Section Treatment Section Control Section Treatment Section Mean Stdev. Mean Stdev. Mean Stdev. Mean Stdev. 1 31.80 2.58 27.72 5.08 27.22 4.37 23.64 5.55 2 30.87 3.38 29.18 2.63 26.75 6.66 25.87 8.33 3 30.08 3.02 28.25 4.02 29.14 5.47 25.76 5.43 4 N/A N/A N/A N/A 29.12 4.52 26.78 8.15 Mean 30.92 2.99 28.38 3.91 28.05 5.25 25.51 6.86 Table 2: Control vs. Treatment sections: Results of the team-based efficacy questionnaires (TEQs) Session

It is observed that students in the control section approved of their team-based activities more than the students in the treatment section. There are two possible explanations. First, the ease of face-to-face interactions gave the impression that the team was doing better, which is consistent with our earlier observation with the peer rating results. Second, how the student agents form their teams did not necessarily meet the students’ preference. Note that a student did not have access to other survey results, including how his or her team members thought of him or her as a peer. However, the student agent did and perused this information in its bidding for the most useful or compatible team. Finally, the correlation between a student’s performance and the other parameters is investigated, as shown in Table 3. First, it is observed that the treatment section had higher correlation values in SEQ (0.41 vs. 0.28), PRQ (0.34 vs. 0.23), and TEQ (0.50 vs. 0.22) than did the control section. This indicates that the better students (with higher post-test scores) in I-MINDS teams rated their self-efficacies better, rated their peers better, and rated their team-based efficacies better than those in the traditional face-to-face teams. Looking more closely at how I-MINDS students interacted, we see that students who had better post-test scores were also the students who sent longer messages (with a correlation of 0.40). Thus, in this case, better students assumed a larger role in their respective teams during the treatment. Combining this observation with what has been reported earlier on the average

190

N. Khandaker et al. / Student Learning and Team Formation in a Structured CSCL Environment

normalized post-test scores, there are indications that better students helped other students do better in the treatment section and that resulted in better individual performances as evidenced in the post-test scores. Spring 2005 Fall 2005 Correlation with Post Test Correlation with Post Test Control Section Treatment Section Control Section Treatment Section SEQ 0.28 0.41 0.22 0.16 PRQ 0.23 0.34 -0.01 0.11 TEQ 0.22 0.50 0.09 0.12 Number of Messages Sent N/A 0.11 N/A 0.27 Avg. Length of Messages N/A 0.40 N/A 0.25 Table 3: Correlation between Post-test Score and Other Parameters Variable

However, the above observations were not repeated in the Fall 2005 study. Comparing the two sections, how students did during the collaborative learning activities did not correlate with how they performed individually in the post-tests. Compared to Spring 2005, better-performing students in Fall 2005 tended to send more messages (0.27 vs. 0.11), but shorter messages (0.25 vs. 0.40). Does that mean that the better-performing students in Fall 2005 were less patient with their peers? Further, students in the Spring 2005 treatment section reported a 0.41 correlation between their self-efficacies and their post-test scores, compared to only 0.16 in the Fall 2005 treatment section. That means that the students in the Fall 2005 treatment section were far less accurate in their knowledge of their own ability to solve the upcoming problem set, which is very important to form effective teams: students who think they are good at a particular topic when in fact they are not as good can misguide the team activities. This indicates that even though a student is doing very well individually, he or she may not be helpful to other team members. Therefore, when forming teams of learners, the individual competence is not the only factor, the compatibility and the history of the members working together should also be considered. Indeed, I-MINDS’ team formation algorithm—VALCAM—does consider the PRQ values while forming teams. However, since this algorithm uses reinforcement learning, it needs some training before it could form effective cooperating teams. The quality of interaction between the teammates depends on various things, their likings of each other, their expertise in the problem and the difficulty of the assigned problem. This last factor is vital because if the problem is too easy, interaction among team members becomes a liability instead of being an asset. From our close observation of the students, it was observed that more students in Fall 2005, on average, found the assigned problems to be easy, than those in Spring 2005—as they also achieved better course grades. Therefore, this could be a very possible reason for the lack of impact of structured collaborative learning (both control and treatment) in Fall 2005. This hints that a learner team in a CSCL environment could work better only when the problem is too difficult for one team member to solve by himself or herself. This could then also motivate students to exchange messages to help each other obtain a solution. As a whole, the findings of our experiment can be summarized in the following way: x From the user’s perspective, the conventional structured face-to-face collaborative learning is more preferable than the structured computer-supported collaborative learning. The higher TEQ and PRQ values of the control group point to that. However, the students’ perspective of teamwork is affected by various factors like ease of use of the communication tool, variety of communication methods, etc. I-MINDS only allowed students to communicate with each other using text messages. However, in face-to-face interaction, the students worked with each other in person. So, it is not fair to compare face-to-face interaction with simple text messaging. Considering this, we are now

N. Khandaker et al. / Student Learning and Team Formation in a Structured CSCL Environment

x

x

x

x

191

working on an advanced whiteboard in I-MINDS that would let the students work with their team members more closely and through more intuitive ways than text messages such as equations, flowcharts and handwriting/hand-drawing. Our results indicate that structured computer-supported collaborative learning helps students learn better than conventional structured face-to-face collaborative learning. The higher normalized post test score of the treatment section point to that. Since both the control and treatment groups followed the Jigsaw process, this observation may be attributed to the fact that the restricted mode of I-MINDS’ communication forced the students of the treatment section to be more explicit and articulate in expressing their ideas. So, they were able to gain a deeper understanding of the subject matter than the control section students. To gain further understanding, we are working on a message classification system that would allow us to understand how a student’s understanding or expertise in the given problem changes over time during a session. Our results indicate that a student’s self-efficacy assessment does not correlate with his or her performance in either setup. Since the students are solving problems in an area which is new to them, it is reasonable that they may not be able to judge their own performance accurately. However, our team formation algorithm uses the self-efficacy assessment of a student while forming teams as a measure of that student’s potential ability. So, we will look into lowering the weight or importance of this measure in the team formation algorithm (VALCAM), thus giving more importance to tractable measure (such as the quality of questions asked) that we use for computing a student’s competence. Further, we will also include past individual performance (e.g., post-test scores) of a student to help refine the self-efficacy measurement. According to our results, individual performance of a student is not correlated to his or her impression about the usefulness of his or her team. In some situations, the good students view the teamwork as not useful and the not-so-good students view the teamwork as useful and vice versa. The low correlation between a student’s performance and his or her TEQ values point to that. In general, if a strong student can solve the assigned problems without the help of the team members, he or she may perceive teamwork as unnecessary. As a result, cooperation or interaction between both parties suffers, resulting in low valuation of the teamwork (TEQ). So, students rate their teams lowly regardless of their own performance, generating low correlation values. To gain further insight into this correlation, our future experiments will involve challenging tasks or problems that can only be (better) solved through effective teamwork. Finally, our results indicate that, individual performance value of a student is not correlated with the peer review received by that student. A good student can receive a low review from his or her peers and vice versa. This happens because in teamwork, a student’s helpfulness is very important in determining how his or her peers perceive him or her as a team member, regardless of the student’s individual capabilities. The team members’ perception of each other contributes to their compatibility. The team formation algorithm (VALCAM) used PRQ values to measure the compatibility of the team members; it was not very successful because of two reasons. First, VALCAM uses reinforcement learning and thus requires more than three or four iterations to form compatible teams, whereas our experiments had only three or four iterations. Second, PRQ value alone may not be a sufficient measure of compatibility. For our future experiments, we will build a more precise and accurate compatibility measure that would model student dialogues, and semi-structured activities on digital whiteboards. This revised compatibility measure would help VALCAM build better teams.

192

N. Khandaker et al. / Student Learning and Team Formation in a Structured CSCL Environment

3. Conclusions and Future Work

In this paper, we have presented the results of our two-semester long experiment with I-MINDS, a structured CSCL delivery tool. Our results indicate that even though students prefer conventional structured face-to-face collaborative learning over computer-supported structured collaborative learning, working in the latter may improve their performance or learning. Furthermore, the results indicate that in both conventional face-to-face and computer-supported structured collaborative learning, a variety of factors affect the performance of a learner team. These factors include difficulty of the assigned problem and compatibility of team members. Therefore, these factors should be taken into account to build successful teams in structured collaborative learning environment. Acknowledgments

The authors would like to thank the National Center for Information Technology in Education and NSF (SBIR grant DMI-044129) for the research funding. The authors would also like to thank X. Liu, P. Vemuri, S. Namala, and X. Zhang for their programming work. References [1] Aronson, E., N. Blaney, J. Sikes, C. Stephan, and M. Snapp (1978). The Jigsaw Classroom, Beverly Hills, CA: Sage. [2] Beaudoin, M.F. (2002). Learning or lurking? Tracking the “invisible” online student. The Internet and Higher Education, 5(2), pp. 146-155. [3] Clarke, J. (1994). Pieces of the Puzzle: The Jigsaw Method. In S. Sharan (ed.) Handbook of Cooperative Learning Methods, Westport, CT: Greenwood Press. [4] Chalmers, C. & Nason, R. (2005). Group Metacognition in a Computer-Supported Collaborative Learning Environment. Proc. ICCE’05, Singapore, Singapore, pp. 35-41. [5] Chang, Y-W., Ching, E., Chen, C-T. & Chan, T-W. (2005). Computer-Mediated Face-to-Face Interaction Supporting for Peer Tutoring. Proc. ICCE’2005, Singapore, Singapore, pp. 51-58. [6] Daradoumis, T., Guitert, M., Giménez, F., Marquès, J.M. & Lloret, T. (2002). Supporting the composition of effective virtual groups for collaborative learning. Proc. ICCE’02, Palmerston, New Zealand, pp. 332-336. [7] Ikeda, M., Go, S., and Mizoguchi, R. (1997). Opportunistic group formation. In: Boulay, B. d. and Mizoguchi, R. (eds.) Proc AIED’97, IOS, Amsterdam, Netherlands. [8] Johnson, D. & Johnson, R. (1999). Making co-operative learning work. Theory into practice, 38(2), pp. 67-73. [9] Koschman, T., Hall, R., & Miyake, N. (Eds). (2002). CSCL 2: Carrying forward the conversation. Mahwah, NJ: Lawrence Erlbaum Associates. [10] Liu, X., Zhang, X., Soh, L.-K., Al-Jaroodi, J. & Jiang, H. (2003). A Distributed, Multiagent Infrastructure for Real-Time, Virtual Classrooms. Proc. ICCE’03, Hong Kong, China, pp. 640-647. [11] Mulder, I., Swaak, J. & Kessels, J. (2002). Assessing group learning and shared understanding in technology-mediated interaction. Education Technology & Society, 5(1), pp. 35-47. [12] Picciano, A. G. (2002). Beyond Student Perceptions: Issues of Interaction, Presence, and Performance in an Online Course. Journal of Asynchronous Learning Networks, 6(1), pp. 21-40. [13] Ruelland, D.; Brisebois, A. (2002). An electronic performance support system for the eLearner. Proc. ICCE’02. Palmerston, New Zealand, pp. 1072-1076. [14] Soh, L-K. (2004). On Cooperative Learning Teams for Multiagent Team Formation, in Technical Report WS-04-06 of the AAAI’s 2004 Workshop on Forming and Maintaining Coalitions and Teams in Adaptive Multiagent Systems, San Jose, CA, pp. 37-44. [15] Soh, L-K., Khandaker, N. & Jiang, H. (2006a). Multiagent Coalition Formation for Computer-supported Cooperative Learning. Proc. IAAI’2006, Boston, Massachusetts, pp. 1844-1851. [16] Soh, L-K., Khandaker, N., Liu, X. & Jiang, H. (2006b). Computer-Supported Cooperative Learning System with Multiagent Intelligence. Proc. AAMAS’2006, Hakodate, Japan, pp. 1556-1563. [17] Soh, L-K., Khandaker, N., Liu, X. & Jiang, H. (2005). Computer-Supported structured cooperative learning. Proc. ICCE’2005, Singapore, Singapore, pp. 428-435.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

193

An Integrated Framework for Fine-Grained Analysis and Design of Group Learning Activities Seiji Isotani, Riichiro Mizoguchi The Institute of Scientific and Industrial Research Osaka University, Japan [email protected] Abstract: To evaluate the teaching-learning process in collaborative learning sessions and its educational benefits for learners, we should analyze the interaction process during each session and select appropriate learning goals and tasks for each learner. However, the interaction process is very difficult to analyze, even for experts, and furthermore choosing appropriate goals/tasks can be even more challenging. The main objective of our work is to construct a conceptual structure based on ontology to help the interaction analysis and the learning design. We aim to make the tacit benefits for the learners explicit identifying the relationships among interactions and educational benefits. Through this conceptual structure we show how it is possible to analyze and design effective collaborative learning sessions proposing tasks and goals with justification by learning theories. Keywords: Collaborative learning, ontological engineering, instructional design, interaction analysis

Introduction Nowadays, collaborative learning (CL) has become a method increasingly popular used by teachers in classrooms and in e-learning environments. In spite of that, to design effective CL sessions or to analyze the interaction processes among learners, capturing what really happens in each session, has been a very complex job due to a lack of understandable models for representing what is going on [11]. Although there are many research efforts related to the evaluation/analysis of CL sessions, many just consider the quality of the group’s result as a "success" criterion [3]. Nevertheless, according to Dillenbourg [6], the key to understanding collaborative learning is to gain an understanding of the wealth of interactions among the individuals. Therefore, to provide an effective CL session, establishing parameters (goals and tasks) appropriate for each learner, we need comprehensible models to represent a CL session based on interactions among individuals. To deal with the problems presented above, our research requires techniques of ontological engineering to, based on learning theories, clarify the benefits of interactions among individuals during CL sessions. In this work we focus on unifying the models of interaction processes (sub-section 1.1) and learner’s growth (sub-section 1.2), presented respectively in [11] and [12], making the relationship among the desired interaction patterns with the learner’s knowledge acquisition process and the skills development process during a CL session. Through this unification, we intend to help design effective CL sessions by providing: (a) a simple and effective way to select tasks and goals for each learner and estimate their educational benefits; and (b) offering a guideline for blended learning, allowing the designer to combine different learning theories (such as Cognitive Apprenticeship, LPP, Peer Tutoring, etc.) to achieve some desired goal. This paper is organized as follows: First, we introduce two previous models to represent collaborative learning in terms of interaction patterns and learner’s development.

194

S. Isotani and R. Mizoguchi / An Integrated Framework for Fine-Grained Analysis

Next, we propose a new model which unifies the previous models and overcome some limitations of using them separately by offering new alternatives for designing, guiding and analyzing CL sessions. And finally, we present the conclusions of this work. 1. Models to Represent CL Session The goals of many researches in CSCL (Computer Supported Collaborative Learning) include to analyze the interaction processes, considering the interaction among individuals, and to identify their educational benefits [2, 3, 6]. However, it is not common to find models that allow the explicit representation of these processes and what is much more difficult is to find such models that represent the relationship among interaction processes and educational benefits based on learning theories. Such models enable the sharing of findings and the use of computers to support the analysis and design of effective CL sessions. The objective of the following sub-sections is to present the models developed by Inaba et al. which aid the explicit representation of a CL session in such a way that it can be understood, analyzed and shared by teachers, or even by computers. The first sub-section presents vocabularies and a model to represent the interaction processes among learners. The second presents a simplified model to represent the processes of knowledge acquisition and development of skills by the learner. These models will be used in the succeeding sections as foundations of a proposed framework for design and analysis of group activities. 1.1 A Model for Interaction Process To represent the interaction process, Inaba et al. [11], prepared two types of vocabularies: utterance-labels and utterance-types. To label each interaction, we needed a vocabulary at a concrete level (utterance-labels). On the other hand, to characterize a CL session easily, we needed a vocabulary at an abstract level (utterance-types). To satisfy this contradiction, Inaba et al. collected great amounts of data in several CL sessions, and together with other CSCL researchers, defined labels to represent the interactions among the users (utterance-labels). Beside that, through analyses of these labels were created groups of labels, called utterance-types, to represent the interaction process at an abstract level and to distinguish and to characterize each type of CL session. Through the definition of these vocabularies it is possible to define interaction patterns in seven types of interaction processes inspired by learning theories. Figure 1 shows an example of interaction pattern used in Cognitive Apprenticeship [4]. In this example, the interaction patterns are represented with labeled boxes (tasks/interactions described through the use of utterance-types) linked with possible transitions: necessary transitions (solid line), or desired transitions (dotted line). Utterance-type expected to “apprentice” role-player

Request to show the way to solve a problem Prompting

Utterance-type expected to “master” role-player

Showing problematic issues Prompting

Teaching the way to solve a problem

Start Start

Final Final Showing the way To solve a probem

Acceptance

Understanding

Necessary Transitions Desired Transitions

Showing the way to solve a problem

Figure 1: Example of interaction pattern: Cognitive Apprenticeship

When such models as presented in Figure 1 are available, we can explicitly represent typical interaction patterns, and thus, it is possible to compare any interaction process with interaction patterns inspired by learning theories [11]. With the construction of such a model for each desired learning theory we can determine whether the CL session was successful,

S. Isotani and R. Mizoguchi / An Integrated Framework for Fine-Grained Analysis

195

based on the learners' interactions, and estimate the educational benefits for each learner. 1.2 Learner’s Growth Model (LGM) The Learner's Growth Model (LGM) developed by Inaba et al. [12] represents, in a simplified way, the learner's knowledge acquisition process and skill development process, explaining the relationships between learning strategies and their respective educational benefits. For such representation we have to explain more about two processes: learning of knowledge and development of skill. The process of acquiring specific knowledge includes three qualitatively different kinds of learning: accretion, tuning and restructuring [14]. Accretion is to add and to interpret new information in terms of pre-existent knowledge. Tuning is to understand knowledge through its application in a specific situation. Restructuring is to consider the relationships of acquired knowledge and rebuild the existent knowledge structure. Considering the development of skills, there are also three phases of learning: the cognitive stage (rough and explanatory), the associative stage and the autonomous stage [1]. The cognitive stage involves an initial encoding of a target skill that allows the learner to present the desired behavior or, at least, some crude approximation. The associative stage is the improvement of the desired skill through practice. In this stage, mistakes presented initially are gradually detected and eliminated. The autonomous stage is one of gradual continued improvement in the performance of the skill. Inaba et al. [12], developed the LGM model by representing the states of knowledge acquisition and skill development in a graph. However, the original LGM model does not represent the state of restructuring knowledge. Thus, to allow the representation of all states, we worked on improve the LGM model and the result is showed in Figure 2. There are twenty states to represent the levels of the learner’s development at a certain moment of learning. Each state is represented by two triangles. The upper-right triangle represents the state of knowledge acquisition, while the lower-left triangle represents the state of skill development. The arrows show possible transitions between the states and s(x,y) is the simplified form of representing these states in our model: x represents the current state of skill development and y represents the current state of knowledge acquisition. For instance, s(0,0) represents the state where a learner does not have any knowledge or skills to use this knowledge; and s(0,1) represents the state of knowledge acquisition is accretion and the state of skill development is nothing. Using this model it is possible to represent educational benefits of several learning strategies based on learning theories as paths on a graph. Such representation will be explained in details on sub-section 2.2. S(0,0)

[Stages of Skill development]

S(2,0)

S(0,1)

nothing (0) rough cognitive stage (1)

S(1,0)

explanatory cognitive stage (2)

S(3,0) S(0,2)

S(1,1)

associative stage (3) S(3,1)

autonomous stage (4)

S(2,1)

S(4,0) [Stages of Knowledge acquisition]

S(0,3)

S(2,2) S(1,2) S(3,2)

nothing (0) S(4,1) accretion (1)

S(2,3)

tuning (2) restructuring (3) S(1,3) S(3,3)

S(4,3)

S(4,2)

Figure 2: Learner’s Growth Model (LGM)

196

S. Isotani and R. Mizoguchi / An Integrated Framework for Fine-Grained Analysis

2. Unifying the Models: Building and Merging Until now, with the developed models presented in section 1, it is possible to successfully identify which kind of collaboration occurs in a CL session, understanding the essence of the group's interactions (sub-section 1.2), and to estimate the expected educational benefits for each member (sub-section 1.3). Nevertheless, there are some limitations when these two models are not unified: 1. There is no relation among interactions and learner’s growth; 2. We can not explain a path in the LGM graph through a set of events; 3. Difficulty to blend learning theories based on the models. The meaning of blend theories is to blend different learning strategies producing a better learning process; 4. There is no way to intervene while a session is taking place. For example, if a learner, who has a misunderstanding, teaches another learner, he will transfer his problem to the other learner from the beginning until the end of the session. Our propose is to unify these two models extending the Collaborative Learning Ontology [9], which represents the CL process and works as a common vocabulary. We are aiming at supporting the design and analysis of CL processes by representing and storing models of CL in terms of ontologies. Unifying these two models helps to overcome the difficulties addressed above by clarifying the relationships among interaction patterns, learning strategies and learning goals. Furthermore, we believe unifying these models is the first step to explain what a learning theory is, making tacit characteristics explicit: for instance, clarifying expected benefits, use restrictions, guidelines for leading/performing activities, in addition to other important aspects of the teaching-learning process. 2.1 Building the Foundations Influential I_L event

I event Learning Theory

*

Instructional event

Learning Strategy Y<=I-goal

Means to achieve the goal

Instructor Agent

I-role Agent

Instructional action

Agent

Benefits for the Instructor

Action

You-role I-goal G

*

I-goal

Teaching-Learning Process Interaction Pattern Necessary Interaction Activity

* *

Influential I_L event

Desired Interaction Activity Influential I_L event

(a)

I-goal

L event Learning event

Learner Agent

Learning action Action

Benefits for the Learner I-goal

(b)

Figure 3: Example of interaction pattern: Cognitive Apprenticeship

To unify the models, first we represent the interaction patterns using a conceptual structure called Influential I_L event (Figure 3b) and after we propose a conceptual structure for representing an excerpt of the conceptual structure of Learning Theory (Figure 3a), which unifies the models. Using the Influential I_L event structure we divided the interaction process in two events: instructional event and learning event. Every instructional event has a reciprocity relationship with the learning events. In other words, during the teaching-learning process, when a person speaks, the other listens; when someone asks a question, the other answers; and so on. Each event has a corresponding action (or actions) and its possible educational benefits to the initiator. These actions and educational benefits are directly related to the context (learning theory) in which the events and the learning strategies are executed. The representation of the conceptual structure of Learning Theory in Figure 3a consists of two main parts: the Learning Strategy and the Teaching-Learning Process. The

S. Isotani and R. Mizoguchi / An Integrated Framework for Fine-Grained Analysis

197

Learning Strategy specifies how (Y<= I-goal) the learner (I-role) should interact with other person (You-role) to achieve his objectives (I-goal). For instance, in Cognitive Apprenticeship a learner interacts with other learners to guide him during the resolution of a problem. In this case the learning strategy (Y<= I-goal) used by this learner is "learn by guiding", his role (I-role) is known as "master role", the role of the learner who receives the guidance (You-role) is known as "apprentice role", and the goals of the learner who guide (I-goal) are to acquire cognitive skills (and meta-cognitive skills) at an autonomous level. Previous works of Inaba et al. [9, 10] show the strategies (Y<= I-goal), learner’s roles (I-role and You-role) and individual goals (I-goal) of several learning theories. The Teaching-Learning Process specifies the interaction pattern of a learning theory represented by the necessary and desired interaction activities (processes) among two people (for instance, master and apprentice). As we mentioned before, we can describe interactions using the I_L event for explicitly represent the interaction and its benefits from both points of view: for those who do the action and for those who receive the action. Thus, to specify the teaching-learning process we mapped the interaction pattern presented in section 1 to fit in our influential I_L event structure. At present, with this mapping, we identified more than 13 I_L events and its respective benefits used by seven different learning theories: Cognitive Apprenticeship [4], Anchored Instruction [5], Peer Tutoring [7], Cognitive Flexibility [16], LPP [13], Socio-Cultural Theory [17] and Distributed Cognition [15]. Table 1 shows some I_L events used by Cognitive Apprenticeship (CA) and Anchored Instruction (AI), and their expected benefits for instructor and learners. Table 1. Some Influential I_L events and its benefits in the context of two learning theories Influential I_L events Affirmative reaction

Clarify the problem Demonstration of how to solve a problem Instigating thinking

Monitoring

Event (Instructor/Learner) Acceptance/ Understanding Identifying learner’s problem/ Externalization of problem Demonstration/ Observing demonstration Argumentation/ Analyzing arguments

Checking/ Carrying out a task

Notifying how the learner is

Giving information/ Processing information

Requesting problem’s details

Asking about problematic understanding/ Pointing out problematic understanding

Setting up learning context

Set information context/ Contextualization of information

Showing a solution

Explanation/ Understanding explanation

Learning Theory

Expected benefits (I-goal) Instructor Learner

CA

s(3, 2) ĺ s(4, 2)

s(2, x) ĺ s(3, x), x=0,1,2

AI

s(2,y) ĺ s(3,y), y=1,2

s(x,1) ĺ s(x,2), x=1,2,3,4

CA

s(3, 2) ĺ s(4, 2)

s(0, x) ĺ s(1, x); s(1, x) ĺ s(2, x), x=0,1,2

CA

s(3, 2) ĺ s(4, 2)

s(0, x) ĺ s(1, x); s(1, x) ĺ s(2, x), x=0,1,2

CA

s(3, 2) ĺ s(4, 2)

s(1, x) ĺ s(2, x), x=0,1,2

CA

s(3, 2) ĺ s(4, 2)

s(1, x) ĺ s(2, x); s(2, x) ĺ s(3, x), x=0,1,2

AI

s(2, y) ĺ s(3, y), y=1,2; s(z, 1) ĺ s(z, 2), z=2,3

s(x,0) ĺ s(x,1); s(x,1) ĺ s(x,2), x=1,2,3,4

CA

s(3, 2) ĺ s(4, 2)

s(1, x) ĺ s(2, x), x=0,1,2

AI

s(2, y) ĺ s(3, y), y=1,2;

s(x,0) ĺ s(x,1), x=1,2,3,4

CA

s(3, 2) ĺ s(4, 2)

s(2, x) ĺ s(3, x), x=0,1,2

AI

s(2, y) ĺ s(3, y), y=1,2

No expected benefit

CA AI CA AI

s(3, 2) ĺ s(4, 2)

s(0, x) ĺ s(1, x), x=0,1,2

No expected benefit s(3, 2) ĺ s(4, 2) s(2, y) ĺ s(3, y), y=1,2;

s(x,0) ĺ s(x,1); s(x,1) ĺ s(x,2), x=1,2,3,4 s(2, x) ĺ s(3, x), x=0,1,2 s(x,1) ĺ s(x,2), x=1,2,3,4

In spite of the influential I_L event has one main objective it is worth to point out that for each learning theory the same I_L event may have different learning purposes, and

198

S. Isotani and R. Mizoguchi / An Integrated Framework for Fine-Grained Analysis

for this reason, it may have different actions and/or different expected benefits. It happens because each theory is looking for helping the learner concerning different states of knowledge and different states of skill development using different learning resources. For example, although the I_L event “Setting up the learning context” is used to contextualize the learner for a better understanding of the content, as we showed on Table 1, in the context of Anchored Instruction we expect learners to acquire some content specific knowledge, and in the context of Cognitive Apprenticeship we expect learners to develop some skills. 2.2 Merging the Models Observing Figure 3 that with the representation of interaction patterns through I_L events and using our conceptual structure of Learning Theory, we can identify the interactions and their benefits for Instructor and learner in the context of a learning theory, and thus, we realize the unification of the models presented in section 1. This unification can be graphically represented by a path on the LGM graph and associating each graph’s edges with the Influential I_L events which correspond to a specific change of the learner’s state in the graph. In Figures 4 and 5, we use the learning theory “Cognitive Apprenticeship” to demonstrate how we can unify the models, clarifying which activities of the chosen interaction pattern can help the learner's development during different phases of learning. Figure 4 shows the result of mapping the interaction pattern of Cognitive Apprenticeship (Figure 1) into Influential I_L events. The boxes are labeled with one number followed by one I_L event. As in Figure 1 we represent the transitions between boxes as necessary transitions (solid line), or desired transitions (dotted line). Each number in the boxes is used to represent the followed I_L event in our proposed model (Figure 5). 8: Showing a solution

7: Requesting problem's details

6: Instigating Thinking 5: Notifying how the learner is

1:Setting up learning context

2: Demonstration how to solve a problem

4: Monitoring

9: Affirmative reaction

3: Clarifying the problem

Figure 4: Interaction Pattern of Cognitive Apprenticeship represented by Influential I_L events

As one of the results of this work, we show in Figure 5 an example of the unification of the models graphically represented by improving our LGM Model (section 1.2) for Cognitive Apprenticeship augmented by the learning strategy “learning by apprenticeship”. This model is improved by labeling each arrow with specific Influential I_L events (interactions inspired by learning theories) that facilitate the transitions among states. We call this model of GMIP –Growth Model improved by Interaction Patterns. The bold arrows represent the transition from one state to the other which is facilitated through this learning strategy using the labeled interactions; the dashed arrows represent the facilitation of the transition. There are two kinds of interactions: the necessary interactions, represented by a black circle, and the desired interactions, represented by a white circle. The interactions are linked by ellipses. The dashed ellipse represents a directed link between two interactions (I_L events) in Figure 4 and the full ellipse represents a no-directed link between two interactions, it means that in Figure 4 there is a cycle between these two interactions. In the context of Cognitive Apprenticeship shown in Figure 5, the Influential I_L events “1:Setting up the learning context”, “2:Demonstration how to solve a problem” and “3:Clarify the problem” are events to facilitate learners who do not have any cognitive skill, s(0, x), to get some cognitive skills in rough cognitive stage, s(1, x). The same events “2” and “3” above together with “4:Monitoring”, “5:Notifying how the learner is”, and

S. Isotani and R. Mizoguchi / An Integrated Framework for Fine-Grained Analysis

199

“6:Instigating thinking” also facilitate learners with cognitive skills in rough cognitive stage, s(1, x), to achieve the explanatory cognitive stage, s(2, x), and so on for the other stages.

Figure 5: Example of GMIP for Cognitive Apprenticeship Model

The main contribution of our proposed model GMIP is to solve, at least partially, the problems presented in beginning of section 2. This model clarifies, more precisely, how interactions can affect learner’s development, facilitating the learning design based on events. Thus, it becomes a powerful tool helping designers to select events (interactions) and roles for each learner, based on interaction patterns and learning strategies appropriate for desired learning goals and sub-goals (and vice versa). Furthermore, it is possible to offer new alternatives for designing, guiding and analyzing CL sessions. For example: (a) for each sub-goal, it is possible for the teacher to intervene, for guiding learners or analyzing collaboration outcomes while a CL session is not finished, as opposed to adjustments after it has ended, as is usually the case. Observe that we are not trying to say that it is possible to intervene in real time. What we point out is the possibility of split the collaboration in several steps (sub-goals) allowing the teacher’s intervention after each step. Another interesting example is the possibility of blend learning strategies based on our proposed model. It can be done by blending two or more strategies to achieve one desired goal. Thus, using one learning strategy, after achieving a desired sub-goal, we can change for another learning strategy to obtain another desired sub-goal that the first one can not offer. That is, with our model we can realize a guideline for blended learning theories. With the above possible use of the GMIP model in our mind, to (a) provide input data to setup programs for designing CL sessions and to analyze group interactions; and (b) to develop programs for help collaborative learning; we have implemented the conceptual structure presented in section 2.1 using the ontology editor Hozo (available at http://www.ei.sanken.osaka-u.ac.jp/hozo) extending the CL Ontology [9]. 3. Conclusions The possibility of clarifying what a CL session is and to amplify its educational benefits, providing resources that facilitate its representation, design and analysis has been a great challenge. In this paper we used two models previously developed, the Interaction Pattern [11] and the Learner’s Growth Model [12], and worked on clarifying the relationships among interaction patterns, learning strategies and learning goals. As a result, we have

200

S. Isotani and R. Mizoguchi / An Integrated Framework for Fine-Grained Analysis

proposed an integrated model, called GMIP, which unifies the previous models through the development of a conceptual structure which extends the CL ontology and represents an excerpt of the learning theory concept. This model has been implemented using the Hozo ontology editor and can be used to develop programs for CL design and analysis. There are, at least two, main benefits provided by our GMIP model. First, it helps the analysis of group’s interactions contributing to a more precise analysis of a CL session, estimating educational benefits while a collaborative session is not finished. And second, it offers a guideline for blended learning based on learning theories which helps designers to identify more easily the role and kind of interactions (and actions) should be practiced by learners to achieve a desired goal or sub-goal in CL sessions. Our future researches include a study demonstrating some examples and possibilities to blend learning strategies semi-automatically based on GMIP model. This is another step forward in the improvement of ontology-aware authoring systems for collaborative learning that offer help in designing learning activities based on learning theories, while providing an easy way to analyze interactions among learners and to estimate educational benefits. Our ultimate goal is to completely develop such an ontology-aware authoring system. References [1] Anderson, J. R. (1982) “Acquisition of Cognitive Skill”, Psychological Review, 89(4), pp. 369-406. [2] Barros, B., & Verdejo, M.F. (2000) “Analyzing student interaction processes in order to improve collaboration. The DEGREE approach”, Int. Journal of Artificial Intelligence in Education, Vol. 11, pp. 221-241. [3] Collazos, Cesar A., Guerrero, Luis A., Pino, Jose A., Ochoa, Sergio F. (2004) “A method for evaluating computer-supported collaborative learning processes”, Int. Journal of Computer Applications in Technology, Vol. 19, No.3/4, pp. 151 – 161. [4] Collins, A. (1991) “Cognitive apprenticeship and instructional technology”. In: Idol, L., and Jones,B.F. (Eds.) Educational values and cognitive instruction., LEA.. [5] Cognition and Technology Group at Vanderbilt (1992) “Anchored instruction in science education” In: R. Duschl & R. Hamilton (Eds.), Philosophy of science, cognitive psychology, and educational theory and practice, Albany, NY: SUNY Press, pp. 244-273. [6] Dillenbourg, P. (1999) “What do you mena by Collaborative Learnng”, Collaborative Learning and Computational Approaches, Oxford: Elsevier Science, pp. 1-19. [7] Endlsey, W. R. (1980) “Peer tutorial instruction”, Educational Technology. [8] Salomon G. (1996) “Distributed Cognitions: Psychological and Educational Considerations”, Cambridge University Press. [9] Inaba, A., Supnithi, T., Ikeda, M., Mizoguchi, R., Toyoda, J. (2000) “How Can We Form Effective Collaborative Learning Groups?”, Proc. of the International Conference on Intelligent Tutoring Systems, Montreal, June, pp. 282-291. [10] Inaba A., Mizoguchi, R. (2004) “Learner’s Role and Predictable Educational Benefits in Collaborative Learning”, Proc. of International Conference on Intelligent Tutoring Systems, Alagoas, pp. 285-294. [11] Inaba, A., Ohkubo, R., Ikeda, M., & Mizoguchi, R. (2003a) “Models and Vocabulary to Represent Learner-to-Learner Interaction Process in Collaborative Learning”, Proc. of the International Conference on Computers in Education, Hong Kong, pp.1088-1096. [12] Inaba, A., Ikeda, M., & Mizoguchi, R. (2003b) “What Learning Patterns are Effective for a Learner’s Growth?”, Proc. of the International Conference on Artificial Intelligence in Education, Sydney, pp. 219-226. [13] Lave, J., Wenger, E. (1991) “Situated Learning: Legitimate peripheral participation”, Cambridge University Press. [14] Rumelhart, D.E., & Norman, D.A. (1978) “Accretion, Tuning, and Restructuring: Modes of Learning”, Semantic factors in cognition. LEA, pp. 37-53. [15] Salomon, G. (1993). “Distributed Cognitions”, Cambridge University Press. [16] Spiro, R.J., Coulson, R.L., Feltovich, P.J., & Anderson, D.K. (1988). Cognitive flexibility theory: Advanced knowledge acquisition in ill-structured domains. In The tenth annual conference of the cognitive science society. Hillsdale, NJ: Lawrence Erlbaum Associates, pp 375-383. [17] Vygotsky, L.S. (1978) Mind in Society: The development of the higher psychological processes. Cambridge, MA: Harvard University Press.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

201

The Development of a Grouping System in a Collaborative Learning Environment Pao-Ta Yu, Yen-Shou Lai, Chia-Ming Liu, Jenq-Muh Hsu Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan, ROC [email protected] Abstract: This study develops a mobile learning and grouping system based on thinking styles and Bloom’s taxonomy in a collaborative learning environment. The environment takes cooperative learning as the instructional strategy, and Student Teams-Achievement Divisions (STAD) as the cooperative learning method. The purpose is to propose a method of heterogeneous grouping that students not only use mobile learning but also use students’ thinking styles for grouping. Participants in the experiment include three classes of 96 sixth-grade students which are assigned to one control group and two experimental groups. Data collection includes mathematical anxiety questionnaires, learning sheets, and achievement test. The results show that the grouping method has better learning effects than mixed grouping and individual learning. Keywords: Cooperative learning, mobile learning, STAD, thinking styles

1. Introduction 1.1. Background In traditional classrooms, the teaching pattern is the hallmark of teacher-fronted classroom. Teachers usually use chalk, blackboard, and textbook as teaching tools. In recent years, mobile learning is developed. It takes place via the wireless platform such as a mobile phone, PDA, and laptop for teachers and students to build a learning environment. There is a rich and long history of practical use of cooperative learning. Many studies show that Student Teams-Achievement Divisions (STAD) cooperative learning group has different superior than control group in learning effects after teaching [1]. The experimental scopes include many course subjects, and there are positive effects about math learning effects. For this reason, the study tries to divide a class into heterogeneous groups based on two factors, including the question level of study sheet and the style of student. The questions of study sheet are classified with Bloom's Taxonomy. Students are classified with thinking styles. On the other hand, there are few studies for cooperative learning method regarding math anxiety. Therefore, the heterogeneous grouping and peer instructions have become a worth research topic that whether it can effectively decrease students’ mathematical anxiety in the mobile learning environment. 1.2. Literature review Cooperative learning is the instructional use of small groups within the classroom as well as across classrooms [2], so that students work together to maximize their own and each other's learning. It is one of the efficient ways to improve students' ability of thinking, solving

202

P.-T. Yu et al. / The Development of a Grouping System in a Collaborative Learning Environment

problems, combining and using knowledge technology, and students take this spirit of cooperation with them as they go out into the wide world [3]. STAD is one of Cooperative learning strategies. It can improve students' academic achievement, interpersonal relations among the colonies, and attitudes toward learning, schools, peers, and self. Bloom's taxonomy provides a helpful method of improving the exchange of test materials, ideals about testing. It is a classification system of educational objectives based on the level of student understanding necessary for achievement or mastery. In addition, it could be useful in motivating research on examining and on the relations between examining and education. Thinking styles are the theory of mental self-government. The theory of mental self-government addresses the forms of government. When people face many different situations, people choose styles of managing themselves with which they feel satisfied [4]. Table 1 shows the relationship table of Bloom’s taxonomy and thinking styles [5]. Table 1 The relationship table of Bloom’s taxonomy and thinking styles Bloom’s Taxonomy Synthesis Knowledge/ Comprehension Application/ Analysis/ Evaluation

Suitable Question Type Creativity Memory

Suitable Thinking Style Legislative Executive

Analysis/ Macroanalysis/ Microanalysis

Judicial

2. System Architecture The Mobile Classroom and grouping system adopt mobile device such as notebook or PDA. AODV is selected as the routing protocol that used for ad hoc mobile networks. The API of NetMeeting SDK is used in the Mobile Classroom. The Unified Modeling Language (UML) is used to communicate software design during software development activities. STAD Cooperative Learning Method is a combination of in-group cooperation, inter-group competition, and group learning. Students take a regular quiz and groups receive recognition for sum of the improvement scores of group participants. The study sheet is intended to help students prepare for learning material. Grouping method is based on two factors, including the question level of study sheet and the style of students. The questions of study sheet are classified with Bloom’s Taxonomy. Students are classified with thinking styles. After group learning, students take the quiz independently of their mates. Mobile Classroom is a software application that supports group working and group learning. It is used for student-to-teacher or student-to-peer communication through the wireless network. The functions of Mobile Classroom contain Application Sharing, Chat Room, Whiteboard, and File Transfer.

3. Research design and implementation 3.1 Participants and materials The participants were 96 sixth grade students (12 years old in average) selected from three classes in an elementary school. There were six course units in mathematics, arranged by the way of structural instruction method, and the experiment period was about two months. The

P.-T. Yu et al. / The Development of a Grouping System in a Collaborative Learning Environment

203

experimental groups were grouped with the automatic grouping method that proposed by this thesis (class A) and with randomly mixed grouping (class B) in the mobile learning environment. The control group used traditional instruction with individual learning (class C). Therefore, each class used various instruction methods to learn. The Mathematics Anxiety Rating Scale (MARS) was edited to measure the degree of students’ mathematical anxiety. There were 32 questionnaires, including worry, dislike, anxious test, and pressured sense. The questionnaires use five-point Likert scale, and a one-point is the most disagree factor, five-point is the most agree factor. The Cronbach’s coefficient Į was .90, so the reliability of this study was acceptable. ʳ

3.2 System implementation The experiment procedure had five steps: preparing, pretest, the training of cooperation skills and system operating, experiment, and posttest. In the experimental group A, the teacher prepares Thinking Styles Inventory and students take the survey. Next the teacher prepares and classifies the questions of study sheet. The questions of study sheet are classified with Bloom’s Taxonomy. The teacher can use automatic or manual grouping to create new cooperative learning groups. Fig. 1 shows the flow chart of the grouping procedure.

Fig. 1. The flow chart of the grouping procedure. In the experimental group B, the students were grouped randomly or grouped by themselves. For the two experimental groups, the teacher creates a mobile classroom and waits for students to join. When all students joined, the teacher provides the whole class with instruction on a particular subject. They can interact with each other by used Application Sharing, Chat Room, and Whiteboard. Students study the subject in their own groups in preparation for a quiz. They take a quiz independently of their group members. The cooperative learning group receives group recognition based on their scores and past average scores. In the control group C, the students accepted the traditional instruction. In order to compare the differences between control and experiment groups for the mobile classroom, one-way analysis of variance (one-way ANOVA) is applied. The significance level of the hypothesis test is 0.1.

204

P.-T. Yu et al. / The Development of a Grouping System in a Collaborative Learning Environment

4. Results The experiment results of mathematics achievement test are shown in Table 2. When we adopt cooperative learning, the mean of the experiment group A is 73.1 (SD= 9.6) and the group B is 70.8 (SD= 9.9). The control group C is 66.6 (SD=12.4). One-way ANOVA shows that there is difference between three groups (F=2.898, P<0.1). A posteriori Tukey HSD method is took for exploratory data analysis. It shows that there is no significant difference between experiment A and B (p=0.657), also between experiment B and C (p=0.293). But there is significant difference between experiment A and C (p=0.05). Table 2 The means (M) and standard deviations (SD) of Mathematics Achievement Test Sample sizes M SD A (Bloom- Thinking) 32 73.1 9.6 B (Random) 32 70.8 9.9 C (Individual) 32 66.6 12.4 Totals 96 70.2 10.9 The Mathematics Anxiety Rating Scale (MARS) shows that there is no difference between the three groups (F=0.46, P>0.1). It means that although mathematics anxiety scores of experiment groups are lower than control group, but it does not reach the statistical difference.

5. Conclusions The prime purpose of the system described in the study is to build mobile learning and students grouping environments into an existing cooperative learning. This system allows the instruction and learning activities don’t limited in the time and space. The teacher and students in this distributed environment can interact freely with each other. During the cooperative learning, the teacher can use the grouping method that Bloom's taxonomy will be a useful tool. It could be helpful in stimulating research on examining and on the relations between examining and education. For the future research, we can extend experiments on different learning areas, students, and teaching time, or extend teaching materials to Bloom’s Taxonomy of Affective Domain and Psychomotor Domain. Finally, it is useful to establish customized m-Learning and instructions management platform that participants can choose their favorite layout to view. Acknowledgments We would like to thank the National Council of Taiwan, R.O.C., for supporting this research under Contract Nos. NSC 4-2524-S-194-005- and NSC 5-2524-S-194-001-. References [1] Slavin, R. E. (1991). Synthesis of research on cooperative learning. Educational Leadership, 48, 71-82. [2] Ligorio, M. B., and Veermans, M. (2005). Perspectives and patterns in developing and implementing international web-based collaborative learning environments. Computers & Education, 45(3), 271-275. [3] George, M. J., and Michael, A. P. (2002). The teacher’s sourcebook for cooperative learning: practical techniques, basic principles, and frequently asked questions. Corwin Press, Inc. [4] Zhang, L. F. (2002). Measuring thinking styles in addition to measuring personality traits? Personality and Individual Differences, 33(3), 445-458. [5] Passig, P. (2003). A taxonomy of future higher thinking skills. Source Informatics in education, 2(1), 79-92.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

205

Students’ Understandings and Attitudes toward Group learning: An Empirical Study ab

Jianhua Zhao a, David McConnell b Department of Educational Research, Lancaster University, United Kingdom [email protected]; [email protected] Abstract: Group learning is a fundamental method to organise learners participating in a learning process, traditionally in the classroom-based environments, and recently in the web-based environments or the blended e-learning environments. The purpose of this study is to examine students’ understandings and attitudes toward group learning in the blended e-learning environments in Chinese Higher Education Institutions (CHEIs). 20 students were involved in a course - “Computer in Education (I)” – and group learning was used to organise the relevant learning and teaching process in a blended e-learning environment. The methodology of this study is action research and the data were collected through questionnaire survey, group interview, and personal blogs. Students’ understandings of group learning were examined and the results reveal big change from absence to familiarity between the beginning and the end of the course. This result also demonstrated that students’ attitudes to group learning were positive. Keywords: group learning, blended e-learning, knowledge building, group knowledge building, action research

1. Introduction Group learning is an effective method to organise students involved in a learning process, showing advantages of cooperative settings over competitive or individual settings, and is commonly used in the classroom-based learning environments (Webb, 1982; Slavin, 1980; Sharan, 1980), and in the web-based learning environments currently (McConnell, 2006; Masten & et al., 2002; Jonassen & Kwan, 2001). However, it is not a commonly used method in Chinese educational settings, because competitive and individual learning approaches are still dominated (Zhao, 2006; Xu, 1996; Zhao & Zhang, 1999). Even though an educational reform in China has taken place in the past decade, the conventional teaching ideology is still powerful and difficult to change within a short time. In this study, we examine students’ understandings and attitudes toward group learning in Chinese higher education in order to identify the reason why it is not commonly used in the Chinese context. We argue that there is no essential barrier for Chinese students engaging in group learning, but the reason is they do not use it properly because teachers and students are still dominated by the lecturing approach. Numerous researchers in the field have explored students’ perceptions (or understandings) and attitudes using different methods. Thompson and Ku (2005) conducted a research to examine Chinese graduate students’ experiences and attitudes toward online learning. The methodology they used was qualitative case study and the results demonstrate that it was an interesting experience for participants, but their attitudes toward this unfamiliar mode of learning were mixed. However, the seven participants in this study came from a research-intensive University in the western USA, so the results may not reflect on Chinese students studying in China.

206

J. Zhao and D. McConnell / Students’ Understandings and Attitudes Toward Group Learning

In this study, we developed a mixed approach to examine students’ understandings and attitudes toward group learning. 2. Methodology 2.1 Participants, the Course, and the Learning Environments Participants in this study were undergraduates at the School of Information Technology in Education (SITE), South China Normal University. SITE is the leading institution in Educational Communication and Technology field in China. 23 students took the course – Computer in Education (I), 3 students withdrew at the middle of the course. Two kinds of the classroom-based settings were used in this study. One setting is a conventional classroom with ICT facilities, such as digital project, wireless audio system, central control system, and digital camera, etc. Another was a network-based computer classroom where provided 32 computers for students. All computers were connected with internet. The online learning environment of this study was WebCL system developed by Networked Education Laboratory of Beijing Normal University. 2.2 Data collection A questionnaire survey was conducted three times in this course. At the beginning of the first class, the first questionnaire was distributed to students. The total number of questionnaires were 23 and 17 of them were collected. The rate of collection is 73.9%. All of the collected questionnaires were valid data for analysis. The second questionnaire was conducted at the middle of the course. The questions were designed mainly to examine students’ understanding of group learning. 19 of them (total number is 20) gave their anonymous response and all the collected questionnaires were valid. The valid rate of this survey is 95%. The third questionnaire was used at the end of course in order to examine students’ overall understanding and attitudes toward group learning. 16 students participated in this survey and 13 of them gave their feedback. The collected questionnaires were all valid and the valid rate is 81.25%. Three groups (group 2, 3, and 4) engaged in the first focus group interview at the middle of the course and two groups (group 1 and group 5) involved in the second one at the end of the course. Interview time for each group was around one hour. All the interview processes were audio-and-video recorded. All students attended the focus group interview actively. 3. Findings and Discussions 3.1 Students have little experience of group learning at the beginning of the course, and their attitudes toward group learning varied In order to examine students’ background about group learning, the question “have you use group learning before” was asked in the first survey. 14 students (63.6%) replied that they had this kind of experiences before and 8 students (36.4%) said they had no experience of it. For those students who had group learning experiences, what were their attitudes toward it? Their statements in the questionnaire introduced their own experiences of group learning. Some students had positive attitudes toward group learning. Kingston: I feel good when I engaged in the group learning process. However, I found it is not ideal approach for individual learning. Zhenzhen: Group learning can let us learn more and know more. Inter-group cooperation can be used to enforce the students’ friendship.

J. Zhao and D. McConnell / Students’ Understandings and Attitudes Toward Group Learning

207

These two students’ viewpoints represent most students who had replied positively. However, their understandings of group learning were still not properly. Actually, they had 15 years learning experiences from school to university, which implies that they form correct understanding of group learning. It is real because there are still not too much tutors like to use group learning to organise their teaching process in Chinese schools, colleges and universities. 3.2 Students’ understandings of group learning improved at the middle of the course, and students presented their positive attitudes to group learning For the question “do you think you completely understand and know how to engage in the group learning activities”, 26.3% students thought they already completely understood it, 52.6% students considered they understood it well, and 21.1% students acknowledged they had a generalised understanding about it. The results demonstrate that students have already understood group learning well. The data from focus group interviews reveal that they have already formed proper understandings of group learning, for example: Pearl: Um … I have no group learning experiences at all before I attended this course. I thought group learning provides a chance to students, which we can meet together. You know, we can learn how to cooperate in a group, um … because we had to finish our group task in time. Otherwise, we could be lost credits. Um … it is a kind of external pressure. I do believe everybody in the group have this pressure. However, you know, when we are familiar each other in a group, we can do our work well.

They had also introduced some problems they identified and experienced in their group learning process: Billy: The big problem for our group learning is time limitation. Um …, it is really boring for us to choose so many courses in a term. However, we had to do it. Otherwise, we would not graduate one year later. Um … I thought it is a big pressure for us, especially for this course. We have not too much time to use in this course

Students’ attitudes toward group learning were positive, such as: Tina: Actually, I like group learning very much, because I can get help from other students. Um … I thought this is a good way to foster cooperative and collective ability. Especially, um … lots of new ideas can be proposed by group. This is a good environment to in-depth understand and explore the knowledge what we learnt.

3.3 Students because familiar with group learning formed in-depth and appropriate understandings of group learning at the end of the course, and their attitudes toward group learning were positive 100% students considered that they had become familiar with group learning at the end of the course. Meanwhile, students presented their in-depth and appropriate understandings of group learning, for example: Tina: Members should positively attend group learning and communication. The time issue for group learning is quite important because the relevant activities should have the enough time to finish. We must have the background for the certain learning events. Otherwise, it is difficult to form the prerequisite of the group learning. Learning ability is important as well for group members. Everybody in the group should know how to communicate well.

The data from focus group interview demonstrate their understandings and attitudes toward group learning, such as: Luorongda: At the beginning of the course, I don’t like group learning at all. I had the experiences to attend group learning before. However, it is really boring! My group-mates have not this kind ability. Nobody likes to give their extra time to the group learning. So the effectiveness of group learning is quite bad. I think the group learning in this course changes

208

J. Zhao and D. McConnell / Students’ Understandings and Attitudes Toward Group Learning

my attitude to it. Currently, I like group learning very much, because everybody in the group is familiar to cooperate each other. Even though the time for us is also limited very much, we all feel we have accountability to do the group work well. This is the big change! So I also think individual accountability is quite important. Everybody in the group must feel they are not dispensable, because they are all important to the group work. Definitely, something is not ideal within the group learning process. Sometimes I have to suffer the time of group learning, because the efficiency and effectiveness are quite lower. However, I think I also can learn from this weakness of our group learning. A Chinese proverb said “the failure is the mother of success!”

4. Conclusions The findings of this study illustrate that these students have not understood the meaning of group learning well at the beginning of the course, but at the middle and the end of the course, their understandings of group learning were well-established. Students presented their diverse attitudes toward group learning at the beginning of the course, and their attitudes to positive at the end of the course. The results also demonstrate group learning is not a commonly used approach in the Chinese educational field, because lots of students had no group learning experiences when they involved in the course at the beginning. The finding they liked group learning at the end of the course reveals that it can be an effective method to organise the learning and teaching activities in Chinese universities in this subject areas. Acknowledgments We thank the students who were engaged in this course of study, and we cannot finish this paper without their contribution. References Boschee, F. (1991). Small-group learning in the information age. Clearing House, 65, 2. Johassen, D., & Kwan, H. II. (2001). Communication patterns in computer mediated versus face-to-face group problem sovling. Educational Technology, Research and Development, Vol. 49, No. 1. Masten, S. J., Chen, K. C., Graulau, J., Kari, S. L., & Lee, K. H. (2002). A web-based and group environment for introductory environmental engineering. Journal of Engineering Education, Vol. 91, No.1. McConnell, D. (2006). E-learning Groups and Communities. Maidenhead: Open University Press. Scharan, S. (1980). Cooperative learning in small groups: Recent methods and effects on achievement, attitudes and ethnics relations. Review of Educational Research, 50, 241~272. Slavin, R. E. (1980). Cooperative learning in teams: State of the art. Educational Psychologist, 15, 93~111. Thompson, L, & Ku, H. Y. (2005). Chinese graduate students’ experiences and attitudes toward online learning. Educational Media International, 42, 1, 33~47. Webb, N. M. (1982). Group composition, group interaction, and achievement in cooperative small groups. Journal of Educational Psychology, 74, 4, 474~484. Xu, Q. (1996). Characteristics and reform of educational administrative system in our country. Journal of Shanghai Higher Education Research, 6, 59~62. Zhang, X. & Zhang, J. (2004). Research on modern educational technology and instructional model. Journal of Wuhan Institute of Technology, No. 6. (in Chinese). Zhao, J., & McConnell, D. (2006). The Differences of Group learning in the Classroom-based and the Web-based Environments: Factor Analysis. In Sheena Banks, Vivien Hodgson, Chris Jones, Bob Kemp, David McConnell and Christine Smith (Eds.), the processing of the Fifth International Conference on Networked Learning. Lancaster: Lancaster University.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

209

The Effectiveness of Knowledge Building through Computer Supported Collaborative Learning among Elementary Students: A Case Study TSE Wing Cheung Alex, LEE Fong Lok, OU Yong Centre for the Advancement of Information Technology in Education, The Chinese University of Hong Kong, Hong Kong SAR, China [email protected] Abstract: This paper presents the third phase of a research project on Computer Supported Collaborative Learning (CSCL) involving students from 81 schools in Hong Kong and mainland China working on the topic of environmental protection. Instead of capturing an overall picture of all the participating schools, this phase of research focused on the effectiveness of knowledge building through CSCL by an in-depth investigation of the learning processes of students in two of the participating elementary schools. Scardamalia’s principles of knowledge building were adopted to evaluate the effectiveness of knowledge building and 1,298 messages of online discussions posted by 153 students over six months were systematically classified, analyzed, synthesized and evaluated. Though some overseas research reported that even young children can meaningfully engage in knowledge building and benefit from the process through CSCL, this study found that the knowledge building was not as effective as expected, still students were found to have the potential of knowledge building in certain dimensions. This study recommends that teachers can provide better support for their students to facilitate them to turn their own ideas into researchable questions for investigation, improve ideas by criticizing the contributions of others, contribute to a wide variety of ideas and build up their own ideas by critically referring and comparing authoritative sources. Keywords: knowledge building, Computer Supported Collaborative Learning, elementary students

Introduction There has been a booming development in Computer Supported Collaborative Learning (CSCL) over the past few years. The pool of professional and research literature increased rapidly. CSCL is one of the hot topics in various international conferences on Information and Communications Technologies in Education (ICTE). It witnessed a drastic development of the topic, revealing its significance in shaping teaching and learning of the world. As its name reflects, CSCL generally refers to a teaching and learning approach in which web-based or network-based platforms, like the Knowledge Forum, is adopted (Law & Wong, 2003). Over a period of time of implementing the CSCL platform, the participants can conduct online discussions collaboratively. CSCL can be particularly useful to facilitate collaborative knowledge building among participants. Such an emerging approach of CSCL can be versatile to suit the instructional needs of a wide variety of teaching and learning contexts, making CSCL more and more popular (MacKinnon, 2004; Chai, Tan & Hung, 2003; Hew & Cheung, 2003). The implementation of CSCL generally targets at nurturing higher-order thinking skills such as problem solving abilities and collaborative refinement of knowledge within a topic or authentic and complex contexts. Therefore, how to evaluate its effectiveness is really challenging to researchers. Meanwhile, CSCL is becoming more and more popular in

210

W.C.A. Tse et al. / The Effectiveness of Knowledge Building

various levels of schools. There are high demands for more in-depth researches to throw light on the issue so as to guide the implementation of CSCL on the right track. Though overseas research found that even young children could meaningfully engage in knowledge building and benefit from the process, there were many reports of implementation problems of CSCL in elementary schools (Law & Wong, 2003; Tse, Lee & Ou, 2006a; Tse, Lee & Ou, 2006b). It explains why this study chooses elementary students as the focus of study. The significance of this research is thereby revealed. 1. Three “I” Project This paper presents the third phase of the research project of CSCL among 81 schools in Hong Kong and mainland China working on the topic of environmental protection. The project is called the Global Learning Community among Primary Education through Interdisciplinary, Interschool and International Project Learning (Tse, Lee & Ou, 2006a; Tse, Lee & Ou, 2006b). It is called the 3I Project below. The students in Hong Kong conducted their project learning through discussion in web-based CSCL platform with the students in mainland China. Instead of capturing an overall picture of all the participating schools, this phase of research focused on the effectiveness of knowledge building through CSCL by an in-depth investigation of the learning processes of students in two of the participating elementary schools. 2. Two Schools of Study In this study, the students in a Hong Kong elementary school conducted their project learning with the students from an elementary school in a city of mainland China, through a web-based CSCL platform between November 2005 and April 2006. The 1,298 messages of online discussions were posted throughout the learning process. As found in the platform, the 153 students of the 2 schools were divided into 12 learning groups. For almost all of these students of Primary Five (Grade 5), it was their first time to learn in such a context and approach under CSCL. 3. Research Design Focusing on the effectiveness of online discussions of knowledge building, this case study of two schools mainly relied on online platform observations after the end of online discussions. It was both quantitative and qualitative. The quantitative approach mainly emphasized the descriptive analysis of the statistics from various online activities while the qualitative approach stressed the evaluation of the content of each message posted by the students. In this study, 1,298 messages were classified, analyzed, synthesized and evaluated. Scardamalia‘s principles of knowledge building were adopted to analyze the messages and the online interactions (Scardamalia, 2002). To maintain the credibility of the study, besides adopting data of online platform observations, focused-group student interviews and teacher interviews were also conducted among teachers and students of 2 schools. 4. Conceptual Framework: Knowledge building 4.1 Definitions of knowledge building What is knowledge building? Bereiter and Scardamalia created this phrase, referring it as a process of creating new cognitive artifacts resulting from collective discussion and synthesis of ideas. Such process of collective inquiry can lead to a “deeper understanding” through interactive questioning, dialogue and continuous improvement of ideas (Scardamalia, 2002). It is worth making the remark that knowledge building emphasizes a sense of “we” superceding the sense of “I.” In other words, participants should feel that the group is operating collectively and not just as an assemblage of individuals. Throughout the learning process of knowledge building, both understanding and collaboration are emphasized (Bereiter, Scardamalia, Cassells & Hewitt 1997; Scardamalia & Bereiter, 2003). 4.2 Twelve Principles of Knowledge Building One of the foci of concept of knowledge building is its 12 principles (Scardamalia, 2002). These 12 interrelated but distinguishable principles are briefly explained in Table 1. In this study, the relevant principles were adopted to evaluate the effectiveness of knowledge

W.C.A. Tse et al. / The Effectiveness of Knowledge Building

211

building through web-based CSCL. These principles have been adopted in various researches to evaluate different CSCL projects. They justified the workability and appropriateness of the principles to evaluate the effectiveness of knowledge building (Law & Wong, 2003; Scardamalia, 2002). No.

Principles

1

Real authentic problems

2

Improvable ideas

Students can continuously contribute to improve the collective ideas by criticizing the contributions of others.

3

Idea diversity

Students can contribute to a wide variety of ideas and provide extra information relevant to the problem.

4

Rise above

Students can summarize the ideas of others and work out directions for further study.

5

Epistemic agency

6

Community knowledge, collective responsibility

Students can compare and contrast other’s ideas. High level of this principle: students can deal with problems of goals, motivation, evaluation, and long-range planning. Students can share the responsibility for knowledge advancement by regularly making input responding to the collective goal.

7

Democratizing knowledge,

Students can share their ideas and value the contributions of others without domination by a minority.

8

Symmetrical knowledge advancement

The number of messages within and between groups were counted. In this study, since none of the messages were transferred among the members of various groups, the principle was not discussed in this study.

9

Pervasive knowledge building

10

Constructive use of authoritative sources

Knowledge resources are maximized when students share ideas and learn from each other to create a collective knowledge building environment both within and between communities. Instead of time being set aside after fundamental work is done, knowledge building becomes an integral part of practice among students. Students can build up their ideas by critically referring to and comparing authoritative sources.

11

Knowledge building discourse

Students can share, improve and even transfer their ideas by referring to a good practice of referencing.

12

Embedded and transformative assessment

Students can reflectively conduct self-evaluation and internal assessment as well as review their learning progress.

The activities of continuous idea refinement had been covered by other principles. Therefore, the major concern here just stressed on whether the messages were embedded with a good practice of referencing. The number of these messages was counted and analyzed in this study. For instance, after a period of discussion, a student posted a message and commented that the schoolmates wasted many time in discussing irrelevant topics and urged them to stick to the right track. The number of these messages was counted and analyzed in this study.

ideas,

Explanation of the principles / Criteria for classifying online messages Students can convert their own ideas or authentic problems into questions for investigation.

Examples of message in this study / Method of evaluation

For instance, a student was aware of the low awareness of environmental protection among his fellow schoolmates. He suggested a proposal of survey on the platform and it was supported and commented by other group members. The survey was conducted eventually. The number of these messages was counted and analyzed in this study. For example, a student asked the meaning of “4R”. Another student quickly responded that it referred to “Reduce, Reuse, Recycle and Replace”. Then, two more students supplemented the details of “4R”. As well, the percentages of posted messages read by students were counted. The number of these messages was counted and analyzed in this study. In this study, the investigators counted the number of message representing new idea within a group. These messages should be relevant to the foci of discussion. Otherwise, they were classified as irrelevant. The number of these messages was counted and analyzed in this study. For instance, after a period of discussion, a student posted a message and commented that the group members had overemphasized on single aspect and she suggested discussing another relevant but ignored issue. The number of these messages was counted and analyzed in this study. For example, a student posted a message and criticized a group member who usually asked his group members to meet his request for providing him certain kind of information but seldom posting constructive message. The number of these messages was counted and analyzed in this study. Instead of counting message one by one, this study worked out a, Message Posting Interval Index to evaluate whether the students shared the responsibility for knowledge advancement by posting messages regularly. As well, other messages showing students who valued the participation of the others were also counted and analyzed. Instead of counting message one by one, this study tried to count the divergences among the number of messages that individual students posted on the platform within the groups. The percentages of posted messages read by students were also counted.

This principle emphasizes that knowledge building is not only confined to specific situations or subjects but happens in and out of school. Since this study highly focused on online platform observations, the scope of the principle was much larger than that of this study. As a result, it was skipped in this study and it was a recommended topic for further study. To investigate the issue in depth, in classifying the posted messages, 4 sub-categories were constructed for this principle of knowledge building: “Statement without any backup”, “Statement without authoritative backup”, “Statement with authoritative backup”, “Just copied and pasted.” The number of these messages was counted and analyzed in this study.

Table 1: 12 principles of knowledge building (Scardamalia, 2002; Law & Wong, 2003) 5. Overall picture Table 2 shows the statistics from the online discussion on the CSCL platform among the students of the two schools. Over the six months of online discussions in the CSCL platform, the average number of messages posted by students per group was 108. The average number of messages posted by students per month was 216 pieces. However, not all

212

W.C.A. Tse et al. / The Effectiveness of Knowledge Building

the groups were so active. For instance, though there were 152 posted messages in group 8, the posted messages of group 4 were just 66. Overall, the above figures might only reflect a superficial phenomenon. To evaluate the effectiveness of knowledge building through CSCL, the investigators of this study adopted Scardamalia’s principles and the findings are discussed below one by one. The criteria, based on Scardamalia’s principles listed in Table 1, were adopted for classifying online messages. To increase the reliability of study, after the first round of classification of massages, the classification results were carefully audited. The figures were adjusted afterward. Learning group/ Items

Numbers of students in group

Numbers of messages posted by students

Average numbers of messages posted by students

1 2 3 4 5 6 7 8 9 10 11 12 Highest Lowest Average per group Average per month Total

12 13 14 13 14 12 12 13 13 12 11 14 14 11

111 95 92 66 99 105 145 152 79 136 90 128 152 66

9 7 7 5 7 9 12 12 6 11 8 9 12.1 5.1

12.8

108.2

8.5

--------------

216.3

17.1

153

1298

--------------

Numbers of Average Average Standard Average messages read numbers of percentage of derivation of the Message by students messages posted numbers of Posting read by messages read posted message Interval Index students by students per group (MPII) per group 393 33 29.5% 7.1 2.6 228 18 18.5% 9.6 2.4 175 13 13.6% 8.1 2.3 126 10 14.7% 5.5 2.2 258 18 18.6% 8.0 2.2 286 24 22.7% 6.6 2.8 415 35 23.9% 6.1 3.2 326 25 16.5% 8.4 2.8 249 19 24.2% 4.3 2.1 354 30 21.7% 4.6 3.5 219 20 22.1% 6.5 2.1 325 23 18.1% 6.7 2.5 415 34.6 29.5% 9.6 3.5 126 9.7 13.6% 4.3 2.1 279.5

22.2

20.3%

6.8

2.6

559

44.4

--------------

--------------

--------------

3354

-----------

--------------

--------------

--------------

Table 2: Overall statistics from the online discussion in the CSCL platform in the two schools (MPII is discussed in section 6.4) 6. In-depth Analysis 6.1 Real ideas, authentic problems The principle of Real ideas, authentic problems emphasizes that, in a quality process of knowledge building, students can convert their own ideas or problems into researchable questions for investigation (Scardamalia, 2002). As Table 3 shows, there were 41 messages which fell into this category. The average number of messages per group was only 3.4. It was found that minority students were able to input authentic problems into online discussions. These problems included inappropriate disposal of rubbish near their schools and homes, air pollution from vehicles, overuse of using plastic bags, etc. One of the groups even posted 10 messages in this category. No. of Principles of knowledge building / Items principles 1 2 3

10

Other

Real ideas, authentic problems Improvable ideas Idea diversity Statement without any backup Constructive Statement without authoritative use of backup authoritative Statement without authoritative sources backup Just copy and paste Self-introduction

Total numbers of posted message on platform 41 84 204 132

Average numbers of posted message 3.4ʳ 7ʳ 17ʳ 11ʳ

Maximum Minimum numbers of numbers of posted message posted message per group per group 10 0 8 0 18 0 26 0

116

9.6ʳ

23

60

5ʳ

10

0

296 198

42.6ʳ 16.5ʳ

32 16

0 1

0

Table 3: Statistics from the messages classified by some principles of knowledge building Though some students were able to turn their ideas found in their daily lives into the subject of online discussions, only three cases were found in which the students could turn

W.C.A. Tse et al. / The Effectiveness of Knowledge Building

213

these ideas into a researchable question and conducted investigation systematically. Figure 1 shows one of these rare examples in which a student of mainland China successfully conducted a survey in her school about students’ awareness of environmental protection.

Figure 1: A case of turning students’ authentic ideas into a researchable question for investigation. (The name of the student was intentionally deleted to protect her privacy) 6.2 Improvable ideas Under the principle of Improvable ideas, a quality process of knowledge building should involve a situation in which students can continuously contribute to improve their ideas by criticizing the contributions of others (Scardamalia, 2002). To achieve this, students should regularly read the messages contributed by others and comment on them. However, this study found that most of the students had not read most of the messages posted by other students. As Table 2 shows, the average percentage of posted messages read by students was only 20.3% (ranging from 13.6% to 29.5%). In other words, most of the students had not read about 80% of posted messages. On average, the students only read 22.2 messages out of 108.2 messages in their group. It seriously undermined the possibilities and foundation of improving ideas through online discussion. In fact, there were only 84 messages fell into the category of improving ideas. In some cases, it was found that some smart students did post some good topics or questions which could be further developed. However, it was found that most of these messages were not responded to by the others. In many cases, even though these good messages got responses from the others, some of these comments were either illogical or unreasonable. It reflected the fact that some students might not be able to identify the main and peripheral ideas among the primitive and loose ideas, not to mention achieve synthesis, comparison or evaluation of the posted ideas required by this principle. Overall, the effective cases of improving ideas were limited. It was difficult to reach a quality process of knowledge building. 6.3 Idea diversity The principle of Idea diversity stresses that, in a quality process of knowledge building, students can contribute to a wide variety of ideas and provide extra information relevant to the problem (Scardamalia, 2002). As shown in Table 3, 204 messages representing new ideas were found. Its average number of messages per group was 17. It seemed that the figure demonstrated a wide variety of ideas. However, the principle, in fact, not only stresses the diversity of ideas over the discussion, but also requires the extension of the depth of discussion over the mentioned topics. In this regard, it seemed that the performance of the students could be improved in deepening the ideas that they discussed. Firstly, as mentioned, they had not read most of the posted messages. How could they deepen the ideas of others? Secondly, most of the messages with good ideas were not responded to appropriately. These problems inevitably undermined the foundation of deepening the ideas that the students raised, adversely affecting the quality of knowledge building.

214

W.C.A. Tse et al. / The Effectiveness of Knowledge Building

The fourth principles of knowledge building should be Rise above (0.8) but it will not be discussed in detail in this study. Similar to other principles like Knowledge building discourse (1.2), Epistemic agency (0.6), Embedded and transformative assessment (1), Symmetric knowledge advancement (0), the average number of posted messages in this category per group was around or less than 1. The numbers in brackets show the average numbers of posted messages. It reflected that the online discussions of these two schools were far from meeting the requirements of these principles. In other words, reservations should be made about the effectiveness of knowledge building in this case. 6.4 Community knowledge, collective responsibility Under the principle of Community knowledge and collective responsibility, to reach a quality process of knowledge building, students should be able to share the responsibility for knowledge advancement by regularly making input by responding to the collective goal (Scardamalia, 2002). To evaluate whether the students achieved this principle, as mentioned before, this study worked out an MPII. It refers to the Message Posting Interval Index. The below case illustrates the calculation method of the MPII. The information of a student’s distribution of the number of messages posted on the CSCL platform is presented in Table 4. November 1 message

December 0 message

January 0 message

February 1 message

March 3 messages

April 0 message

MPII 3 message

Calculation method: When a student posts at least one message on the CSCL platform, he or she can get 1 point under the MPII. The maximum point under the MPII for each month is only 1 no matter how many messages a student contributes. Conversely, if a student does not post any message in a single month, he or she gets zero points for that month. After the end of online discussions, the points are accumulated as MPII. This student only posted messages in November, February and March. Therefore, the MPII of the student was only 3. In this study, the MPII was worked out to evaluate whether the students shared the responsibility for knowledge advancement by posting messages regularly. MPII can be counted per day, week or month. In this study, it was counted per month to project an overall picture.

Table 4: A student’s distribution of the number of messages posted on the CSCL platform For this case, the maximum MPII was 6 because it involved 6 months. Table 2 shows that the average MPII of 12 groups was only 2.6 (ranging from 2.1 to 3.5). It reflected that the “active period” of the students to post messages on the CSCL platform was only about two and a half months over the total learning process of 6 months. In other words, students did not regularly input messages on the platform and the performance of the students can be improved by more actively taking up their responsibility for knowledge advancement so as to improve the quality of knowledge building. Numbers of posted messages November

December

January

February

March

362

259

89

74

329

185

Average numbers of message per month per group

30.2

21.6

7.4

6.2

27.4

15.4

Average numbers of message per month per student

2.4ʳ

1.7ʳ

0.6ʳ

0.5ʳ

2.2ʳ

1.2ʳ

Maximum numbers of message per month per student

16

11

11

9

23

10

Minimum numbers of message per month per student

0

0

0

0

0

0

Items Total numbers of message per month

April

Table 5: Distribution of the number of posted messages on the CSCL platform over six months Though there were more messages posted on the platform in November and March, it was necessary to evaluate whether those regular inputs corresponded to the collective goal of the whole group as is requested by the principle of Community knowledge and collective responsibility. Otherwise, a quality process of knowledge building could not be guaranteed. Though the number of posted messages in November was high, as shown in Tables 3 and 5, 55% of messages (198 out of 362) were, in fact, self-introductions by the students. For instance, though a student in Group 7 had posted 10 messages in November, all of them, in fact, were self-introductions. Though such messages of self-introduction might help to build

W.C.A. Tse et al. / The Effectiveness of Knowledge Building

215

up a favourable atmosphere for online discussions, it was necessary to strike a balance between building up an appropriate discussion environment and conducting real discussions (Oshima & Oshima, 2002; Tse et al., 2006a). Otherwise, the interactions on the platform were only a chat room among the students. In fact, it was found that some students even made use of the platform to share their online games. Teachers should supervise such activities because it might adversely affect the atmosphere of online discussions. How about the situation of March? It was found that about 20% of messages posted in March could, in fact, be counted as irrelevant to the topic of discussion. Many students, in fact, mixed up the issues of environmental protection with public hygiene. The numerous messages about Avian Flu were typical examples. In some cases, even though some students could post messages relating to environmental protection, many messages might not be sharp enough to respond to the topic of their group. For instance, the pollution crisis of Harbin in the winter of 2005 became a hot topic among the messages posted by the students. It definitely was an issue of environmental protection, but it was irrelevant for some groups with topics about the habits of environmental protection of their daily lives. Overall, these problems made collective responsibility of knowledge advancement difficult to be reached effectively. The quality of knowledge building was negatively affected. 6.5 Democratizing knowledge The principle of Democratizing knowledge stresses that, to reach quality knowledge building, students should share their ideas and value the contributions of others without domination by a minority (Scardamalia, 2002). However, this study found that there were wide divergences among the number of messages that individual students posted on the platform within the groups. As Table 2 shows, the average standard derivation of the number of posted messages was as high as 6.8 (ranging from 4.3 to 9.6). In other words, the messages posted within the groups, in many cases, were dominated by a minority of group members, especially the students of mainland China. In fact, throughout the online discussion of 6 months, 82 students out of 153 students (54%) had posted less than 10 messages. There were 10 students who even sent none of the messages. It reflected that some students were not eager to share their ideas with others. The contributions of messages among the members were not even. On the other hand, did the students value the contributions of the others? As mentioned before, the low percentage of posted messages read by students reflected that many students generally did not really value each message posted on the platform. These findings demonstrated an unsatisfactory quality of knowledge building in this study. 6.6 Constructive use of authoritative sources Under the principle of Constructive use of authoritative sources, in the circumstances of quality knowledge building, students can build up their ideas by critically referring and comparing authoritative sources (Scardamalia, 2002). To investigate the issue in depth, in classifying the posted messages, 4 sub-categories were constructed for this single principle of knowledge building as shown in Table 3. They were namely, “Statement without any backup”, “Statement without authoritative backup”, “Statement with authoritative backup”, “Just copied and pasted.” This study found that, as shown in Table 3, 132 messages were statements without any backup. For instance, some students just commented “I agree” when they responded to the posted messages without specific reasons. 116 messages were backed up by certain reasons, but authoritative sources were not adopted. Among these 4 sub-categories, the highest number of posted message fell into the category of “Just copied and pasted”. There were 296 messages of these. In other words, though many students could adopt authoritative sources, they could not establish their ideas by critically referring and comparing these sources. It seemed that the students had little intention to examine these authoritative sources. Only 60 messages could achieve this target and most of them were posted by the students of mainland China. The students tried to add value, further interpreting or keep a critical stance towards the sources, but on average, there were only 5 messages per group for this category. Teachers of Hong Kong explained it might be due to the reason that the students of mainland China were generally more proficient in inputting Chinese character through keyboard. As a result, a large number of information in the

216

W.C.A. Tse et al. / The Effectiveness of Knowledge Building

platform was created by copying and pasting web resources that the students found. Most of the students did not digest the information well before posting it on the platform. During the interviews, the students and teachers of 2 sides also confessed this problem. Such an inappropriate style of discussion among the students, in fact, converted the discussion in the platform to just an integration of undigested information instead of the opportunities of knowledge building. It seemed that effort needed to be made to build up the analytical and critical thinking abilities of the students. 7. Discussion and Conclusion This study revealed the unsatisfactory quality of knowledge building through CSCL among the elementary students. At least 5 out of 12 principles of knowledge building could not be reached because the average number of posted messages of these principles was less than or round 1 per group. For those who argue that knowledge building is possible through CSCL among elementary students, they should be made aware of the problems that students encountered in this study (Law & Wong, 2003). Though CSCL is a popular approach, it can simply be a mechanical way of learning. How to make use of it well to facilitate quality learning for students is a challenge for teachers, researchers and educators. This study did uncover some problems of the elementary students. Though the underlying factors behind the problems were not deeply investigated in this study, the poor participation of the teachers should be one of the major factors. This study found that the teachers only posted 55 messages throughout the six months and some of these messages, in fact, were repeated among various groups. The roles of teachers are very important for effective implementation of CSCL. It had been discussed in the first and second phase of this research project (Tse et al., 2006a; Tse et al., 2006b). This study found that students did have potential for knowledge building in certain dimensions. Therefore, this study recommends that teachers can provide better support for their students to facilitate them to turn their own ideas into researchable questions for investigation, improve their ideas by criticizing the contributions of others, contribute to a wide variety of ideas and build up their ideas by critically referring and comparing authoritative sources. In this way, more effective and appropriate scaffolding can be provided to nurture competence of tomorrow’s citizens. References [1] Law, N. & Wong, E. (2003) Developmental trajectory in knowledge building. In B. Wasson, S. Ludvigsen & U. Hoppe (Eds.). Designing for Change in Networked Learning Environments: Proceedings of the International Conference on Computer Support for Collaborative Learning 2003 (p.57-66). Kluwer Academic Publishers, Dordrecht. [2] MacKinnon, G. R. (2004). Computer-mediated communication and science teacher training: Two constructivist examples. Journal of Technology and Teacher, 12 (1), 101-114. [3] Chai, C. S., Tan, S. C. & Hung, D. (2003). Fostering knowledge and building communities through Computer-supported collaborative learning. Paper presented at Herdsa Conference 2003. Christchurch, July 6-9, 2003. [4] Hew, K.F. & Cheung, W.S. (2003). Models to evaluate online learning communities of asynchronous discussion forums. Australian Journal of Educational Technology, 19(2), 241-259. [5] Tse, W.C., Lee, F. L. & Ou, Y. (2006a). Model of Evaluating Educational Multimedia Application for Teaching and Learning. Paper presented at Hong Kong International Information Technology in Education Conference 2006, Hong Kong, 7 February, 2006. [6] Tse, W.C., Lee, F. L. & Ou, Y. (2006b). The Ten Neglects of Teachers in Implementing Computer-supported Collaborative Learning. Paper presented at The 10th Global Chinese Conference on Computers in Education, Tsinghua University, Beijing, 2-5 June, 2006. [7] Scardamalia, M. (2002). Collective cognitive responsibility for the advancement of knowledge. In B. Smith (Ed.), Liberal education in a knowledge society (p. 67-98). Open Court, Chicago, MA. [8] Bereiter, C., Scardamalia, M., Cassells, C., & Hewitt, J. (1997). Postmodernism, knowledge building, and elementary science. Elementary School Journal, (97) 4, 329-340. [9] Scardamalia, M., & Bereiter, C. (2003). Knowledge Building. In J. W. Guthrie (Ed.), Encyclopedia of Education, Second Edition (p. 1370-1373). Macmillan Reference, New York. [10] Oshima, J. & Oshima, R. (2002). Coordination of asynchronous and synchronous communication: Differences in qualities of knowledge advancement discourse between experts and novices. In T. Koschmann (Ed.). CSCL 2: Carrying forward the conversation (p.55-84). L. Erlbaum Associates, Mahwah, NJ.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

217

Computer-Supported Content Analysis for Collaborative Knowledge Building in CSCL Jian Liao, Yanyan Li, Ying Zhou, Ronghuai Huang, Jingjing Wang Knowledge Science & Engineering Institute, Beijing Normal University, 100875, Beijing, China [email protected] Abstract ˖ Interaction analysis plays an important role in computer-supported

collaborative learning. This paper proposes an interaction analysis model CSCAC composed of three dimensions: group building level, member contribution, member support, which lays the foundation for interaction content analysis. Based on the

model and domain ontology, this paper explores the text mining and semantic analysis technologies to automatically analyze the interaction content for

collaborative knowledge building. An experiment is conducted to compare the auto-analysis result with that of manual analysis, which shows the the proposed approach is feasible and effective mostly to automatically analyze the interaction content in CSCL.

Keywords: CSCL, Collaborative Knowledge Building, Content analysis, Domain ontology, Concept Map, Keyword extraction , Hownet

Introduction With the development of e-learning, Computer-Supported Collaborative Learning˄CSCL˅ has drawn more and more attention of researchers. In the process of collaborative learning, the validity of evaluation to group collaboration is an important but difficult task. Henri˄1991˅is the first person who put forward an analysis model of understanding interaction contents.[1] Newman(1995) et al. design a content analysis model based on Henri, including the following ten dimensions: Relevance, Importance, NoveltyǃBringing outside knowledge/experience to bear on problemǃSticking to prejudice or assumptions, Ambiguities: clarified or confused, Linking ideas: Interpretation, Justification, Critical assessment, Practical utility (grounding), Width of understanding... And they use the X X

+

− X + X

−

to calculate the degree of cognitive process by each group member or formula the whole group. [2] Besides, Liu (2005), Zhu(1996), Pena-Shaff(2001)ˈN.M.Avouris and other scholars analyze and research on the interaction contents in collaborative learning from different dimensions. +

−

218

J. Liao et al. / Computer-Supported Content Analysis for Collaborative Knowledge Building

1 The Model of Computer-Supported Content Analysis for Collaborative Knowledge Building˄CSCAC˅ On the topic of how CSCL analyzes contents, the important representative scholar in CSCL Koschmann˄1996˅considers research on the meanings and the building processes of meanings in common activities as one of the main research contents of CSCL. Many researchers use “Collaborative Knowledge Building” to explain the building processes of meanings in common activities.[6] Therefore, the analysis of collaborative knowledge building in the collaborative groups is the important stand for setting up the whole content analysis model. The first problem for judging collaborative knowledge building is the knowledge representation. Many researchers regard Concept Map as an effective means of representing and measuring students’ knowledge structures. [6][8] So we can set up the domain ontology of collaborative learning contents by drawing concept maps related to the topic of group discussion. Keywords in interactive texts can be used to represent the individual knowledge of the group members. The relatively important keywords, which are regarded as the representation of group member’s individual knowledge, can be extracted by searching the interactive speeches of members. The group knowledge contain in each group members’, but not the simple sum of all individual knowledge. Some knowledge may be implied by all members, and some may only be in the possession of very few members. Therefore, the knowledge of collaborative groups needs to be analyzed in the holistic perspective. When comparing member or group knowledge with domain ontology, as domain ontology is described by concept and member or group knowledge is described by keyword and the relations between words and concepts are many to many relations. Therefore, we need to carry out semantic analysis of the keywords in order to obtain correct mapping between concepts and words. Member

Contribution Semantic mapping

Member

Semantic

Speech

Member Support

Domain

Ontology

Knowledge Comparison

Group

Knowledge

mapping

Figure 1. CSCAC Model.

Group Building Level

Based on the above knowledge representation and directed by the activity theory, we set up the following model of analyzing collaborative knowledge building, as shown in figure 1.In figure 1, the three circles around the small triangle in the middle represent the three core components of activity theory respectively, which are subject, object and group knowledge. The three sides of the small triangle represent the inter comparison methods

J. Liao et al. / Computer-Supported Content Analysis for Collaborative Knowledge Building

219

between different sorts of knowledge. The comparison between member knowledge and group knowledge or between member knowledge and other group members’ knowledge is carried out by analyzing the contents of their speeches, such as keyword matching method, etc. While the comparison between member knowledge and domain ontology or group knowledge and domain ontology is carried out by semantic mapping method. The three rectangles contained in the large triangle represent the three perspectives for analyzing collaborative knowledge building. The analysis of collaborative knowledge building is stated from three perspectives as follow. 2 Multi-dimensional Analysis 2.1 Level of group collaborative knowledge building (GBL) At present, many researches analyze the level of collaborative knowledge building from the perspectives of “contention degree” and “building degree”. Fisher(1993), Mercer(1995), Coelho˄1994˅and so on consider effective interaction as “exploratory talk”, whose characteristics is that the participants communicate in a critical but constructive way. [6] Therefore, we need to examine both the critical and constructive characteristics of the speech contents. We assume that the more decentralized the speech contents of the group members are, the more critical it will be; and also the closer the speech contents of all group members is compared to the domain ontology constructed by teachers and experts, the more constructive it will be. Definition 1˖Level of Group Collaborative Knowledge Building

GBL = R × U

Where R represents the Relevance of group speeches to the topic. U represents the inconsistence of the speeches among group members. M Definition 2˖Relevance R= L Relevance indicates the relevance of group speeches to the discussion topic, where M represents the number of concepts in the speeches of the whole group which cover the domain ontology. L represents the number of all concepts in the domain ontology. Definition 3˖Inconsistence

U = µ ∑ χ i2 = µ ∑∑ S

i =1

S

N

i =1 j =1

( Fij − F j' ) 2 F j'

Inconsistence means the difference of the speeches among group members, where N represents the sum of keywords in the whole discussion speeches. S represents the number

1 2 µ of the group members. represents an adjustment factor, whose value is S × N . χ i

represents the decentralization degree of the ith member’s speeches compared with the speeches of the whole group. The more the value is, the more inconsistent it will be. Fij represents the frequency of the jth keyword in the ith member’s speeches.

F j'

represents

220

J. Liao et al. / Computer-Supported Content Analysis for Collaborative Knowledge Building

the average number of the jth keyword mentioned by each member in the group speeches. 2.2 Member contributions to collaborative knowledge building We can estimate the contribution of group members to the task by comparing the knowledge of group members and the domain ontology structure presumed by teachers and experts and analyzing the similarity of the two sorts of knowledge. Furthermore, Newman(1995) and Liu(2005) both consider the novelty of members’ speeches and the extension of the topic to be discussed as the important criteria of evaluating the quality of interactive texts. MCi = αRi + βVi + γEi

Definition 4˖Member’s Contribution

Where Ri represents the Relevance of group member I’s speeches to the topic, whose

algorithm is shown as Definition 2. Ni represents the novelty of group member I’s speeches. Ei represents the extension of group member I’s discussion contents. α , β , γ

are adjustment factors, which represent the impact degrees of Relevance, novelty and extension respectively on member’s contribution. And their values are usually as α =0.5ˈ

β =0.3ˈ γ =0.2 respectively.

Definition 5˖Novelty

Vi =

Pi N

This indicates the novelty of member’s speeches, where

Fi

represents the number of

keywords mentioned for the first time by group member i. N represents the total number of keywords. For example, in a discussion, the total number of keywords is 370, among which member A first mentions 118, so we reach the novelty of member A’s speeches as 118/370=0.32. Definition 6˖Extension

Ei =

Ni N

This indicates the extension of member’s speeches, where Ni represents the number of keywords in each member’s speeches. N represents the total number of keywords in the whole discussion speeches. For instance, the number of keywords in member A’s speeches is 207 and the total number of keywords mentioned by all group members is 370, so the extension value is the ratio of 0.56. It can be proved that the values of

MC i , Ri , Vi , E i are all between [0ˈ1].

2.3 Group member’s support and assistance to the other group members The evaluation of the group member’s support to or the assistance from the other group members is done by the comparison of knowledge structure between individual

J. Liao et al. / Computer-Supported Content Analysis for Collaborative Knowledge Building

221

group members. We believe that the more similar of two members’ speeches, the mutual support will be greater. The algorithm of similarity is the same as the vector cosine method which is frequently used in calculating the document similarity in text classification and clustering. Definition 7˖Member’s Support

Sij =

Fi • F j

| Fi | × | F j |

∑F N

=

k =1

ik

× F jk

(∑ Fik2 )(∑ F jk2 ) N

N

k =1

k =1

Where Fi represents the vector of word frequency in user I’s speeches in the vector

space model (VSM). Fj represents the vector of word frequency of keywords in user j’s speeches. Fik represents the word frequency of the kth keyword in the speeches of the ith user. Fjk represents the word frequency of the kth keyword in the speeches of the jth user. N represents the total number of keywords. Therefore, we can obtain the matrix of the degree of member’s support S, which represents the degree of mutual support among group members. And it is also proved that the value of Sij is between [0,1]. 3 Crucial techniques in CSCAC Model 3.1 Building the domain ontology Domain ontology is to describe domain ontology, which is one of the bases for CSCAC model in analysis. It is realized by the concept software—EasyThinking Cognitive Assistant, which is developed by the Knowledge Science and Engineering Institute at Beijing Normal University. In this software, a concept is shown by the node in concept map and described in the word that can best represent the concept. The relation between concepts is shown by the arrow line connecting two nodes. The relations between concepts usually include hypernym, hyponym, attribute, part-of, agent, dative, time, site, etc. 3.2 Semantic mapping between keywords and concepts In order to compare the keywords in members’ speeches with the concepts in the concept map, we need to calculate the semantic similarity of words and those that represent the concepts to be compared with in the concept map. If the similarity is more than a threshold, which is usually taken as 0.8, we will consider the compared words are able to represent the corresponding concepts in the concept map. The semantic similarity can be calculated by How Net, which is knowledge database that mainly digs out the relations between concepts and the characteristics of each concept based on the sememes both in English and Chinese.[9] The semantic similarity between two words can be calculated by How Net and the algorithm can refer to article [10].

222

J. Liao et al. / Computer-Supported Content Analysis for Collaborative Knowledge Building

3.3 Keywords Extraction Keyword extraction is the most basic part of processing natural language.[7][11].As we need to use keywords to represent the knowledge of groups and members, we consider all nouns, verbs, adjectives except some stop words in the text as the keywords. By this definition, the precision of extraction can be considered the precision of the words splitting as. According to the report of National 973 Evaluative Team in machine translation project, the precision in Chinese character has been beyond 97% now, so the extraction is valid. The main factors that affect the experimental results are the correct percentage of word categorization and the degree of word base perfection. So as the experimental shows, we can believe that the keywords extracted by the keyword extraction process are able to represent the knowledge of groups and members. With this algorithm, we develop the module of keyword extraction. 4 Experimental Study In order to prove the validity of CSCAC model in collaborative learning, we design the following experiment. First of all, we develop the tool with C# to support the computation about the Collaborative Knowledge Building. Then we designs a collaborative task on the topic of “Collaborative Composition”, whose content is that each group discuss sufficiently in the chat room on the topic of “Who is the excellent teacher mostly needed in China” according to the assigned materials and compose a short essay of 300 words collaboratively within 1 hour. After this, the teacher designs the domain ontology of the topic of this task, as shown in the concept map of figure 2.

Figure 2. Domain Ontology of Collaborative Task.

Then the teacher logs in the platform to distribute the task to 5 groups. Each group have 4 students, which are selected randomly from a class of 75 students, and organizes the groups to carry out collaborative learning on the task. After the task accomplish, we have a survey in each team about the member contribution. In this survey, everyone will assess the rank of his(her) team member’s contribution according to their impact in this task. We take the means of the result of all members in a team as the member contribution value ranked by manual .In addition, we evaluate manually the interactive text of whole group according to their relevance and inconsistence .After this, we run our tool to statistic all value. The detail is shown in Table1. Thus ,we can compare the rank of Group Building Level and Member Contribution between the manual and auto evaluation result in SPSS. We take Kendall’s W Test to

223

J. Liao et al. / Computer-Supported Content Analysis for Collaborative Knowledge Building

compute the consistence of result between manual and auto evaluation to confirm the reliability of our model. The consistence in evaluation of Group is Kendall's W(a)=0.950 Asymp. Sig.=0.107,And the consistence in evaluation of Member˖Kendall's W(a)=0.840 Asymp. Sig.=0.032. The result proves that the evaluation result in Group Building Level and Member Contribution by CSCAC model is related to the manual evaluation highly. Team

Manua

ID

l Rank

1

5

CSCAC Rank

R

(GBL)

U

Member

Manual Rank

A

5(0.60)

0.76

2

B

0.80

3

C

1

2(0.74)

0.79

0.94

2

1(0.79)

0.78

3

3(0.73)

0.77

4

4(0.65)

0.79

0.28

0.36

3(0.47)

0.69 0.72

0.28 0.20

0.36 0.27

0.31

B

2

2(0.52)

0.77

0.23

0.32

1

1(0.57)

3

3(0.50)

0.77

0.71

0.36

0.24

0.42

0.34

D

4

4(0.47)

0.73

0.17

0.27

B

3

3(0.50)

0.75

0.22

0.29

1

1(0.61)

4

4(0.30)

0.72

0.53

0.45

0.05

0.56

0.08

D

2

2(0.53)

0.76

0.28

0.35

B

2

3(0.50)

0.74

0.22

0.30

3

C

0.83

0.70

0.24

2(0.51)

4

4(0.44)

0.74

0.71

0.25

0.14

0.32

0.20

D

1

1(0.59)

0.75

0.40

0.50

B

2

1(0.61)

0.76

0.42

0.52

A

5

1(0.50)

0.67

C

0.95

E

4(0.47)

A

4

V

2(0.50)

4

C

1.01

R

1

A

3

Rank

(MC)

D

A

2

CSCAC

4

C

4(0.42)

1

D

2(0.52)

3

3(0.47)

0.68

0.77

0.70

0.14

0.24

0.20

0.21

0.33

0.32

Table 1. Experimental Result. 



   7HDP 7HDP 7HDP 7HDP 7HDP

  

0HPEHU$ 0HPEHU% 0HPEHU& 0HPEHU'

   

  

 *%/

5

8

(a) Group Building Level

0&

5

1

:

(b)Member Contribution(Team3)

(c)Member Support

Figure 3 .Visualization of Result in CSCAC

At last, we visualize the result above in bar chart and visualize the Member Support by network graph. These chart can be presented to the members timely. They are shown in figure3. In the figure 3(C), we choose a team’s result randomly as a sample. As the figure illustrates, the edge describes the support among the members. The higher the member

224

J. Liao et al. / Computer-Supported Content Analysis for Collaborative Knowledge Building

support mutually, the wider the edge will be. According to this graph, we can observe the support among the members clearly. 5 Conclusion This paper analyzes interactive text contents automatically by constructing an analysis framework of collaborative knowledge building. This method is able to give timely feedbacks on the collaborative situation of each group in collaborative learning to teachers and students, which will advance further collaborative learning effectively. And the experiment proves it to be practical. The research for the next step is to collect more data to prove effectiveness of each dimension, especially about member support, to promote the automatic analysis results from aspects of perfecting analysis model, elevating the precision of keyword extraction, taking more semantic method such as analyzing the relations between concepts in domain ontology and so on. References [1] Henri, P.(1991).Computer conferencing and content analysis. In Kaye,A.R(Ed.),Collaborative Learning through Computer Conferencing: The Najaden Papers.Berlin:Springer-Verlag,p117-136.

[2] Newman, D.,Webb,B.,& Cochrane,C.(1995).A content analysis method to measure critical thinking in

face-to-face and computer supported group learning. Interpersonal Computing and Technology: An Electronic Journal for the 21st Century,3(2),p56-77.

[3] Ronghuai Huang(2003).The Theories and Methods of Computer-Supported Cooperative Learning 2003

[4] Zhu,E.(1996).Meaning negotiation, Knowledge construction, and mentoring in a distance learning

course. Paper presented at the Proceedings of Selected Research and Development Presentations at the

1996 National Convention of the Association for Educational Communications and Technology, Indianapolis.

[5] Pena-Sha.,J.,Martin,W.,& Gay,G.(2001).An epistemological framework for analyzing student

interactions in computer-mediated communication environments. Journal of Interactive Learning Research,12,p41-68.6

[6] LIU Huang Lingzi; ZHU Lingli; CHEN Yiqin & HUANG Ronghuai,

π A Research about

Collaborative Knowledge Building through Interaction Analysisρ, Open Education Research Vol.11, No.2, P31-37, 2005.

[7] LI Su-Jian WANG Hou-Feng YU Shi-Wen XIN Cheng-Sheng

Research on Maximum Entropy

Model for Keyword Indexing Chinese Journal of Computers Vol. 27 No.9 Sept 2004

[8] Giouvanakis Thanasis; Fragidis Garyfallos; Paschaloudis Dimitrios; Tarabanis Konstantinos; Guoqing

Zhao; Embedding a Vocabulary-based Application for Concept Mapping into a Learning Management System Open Education Research Vol.11, No.5, P38-43, 2005.

[9] Dong, Zhendong (1988) Knowledge Description: What, How and Who? In: Proceedings of International Symposium on Electronic Dictionary, Tokyo, Japan.

[10] QunLiu SujianLi Word Similarity Computing Based on How-net Computational Linguistics and Chinese Language Processing

”( TaiBei, 2002 )”

[11] FENG Jin; LI Chunping Topic detection technology for Chinese text based on statistics and semantic information Journal of Tsinghua University(Science and Technology 2005, Vol. 45, No51 ,P1791

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

225

A Novel Web-based Collaborative Learning Supporting System with Navigation Function Chenyuan Tiana, Zuoliang Chenb, Shigayeshi Watanabec Faculty of Electro-Communications, University of Electro-Communications Chofu-shi, 182, Tokyo, Japan b [email protected] [email protected] Abstract: This paper describes a novel collaborative learning supporting system operating on the web and its experiment data analysis. We insist that in the Web-based Collaborative Learning Supporting (WCLS) system, the individual-paced learning process rather than the group-paced learning process should play the main role. The relationship of individual-paced learning and group-paced learning in WCLS system should be that the group-paced serves the individual-paced. To verify this idea we designed a WCLS, which has two navigation windows; one is Page Navigation (PN), and the other is Discussion Navigation (DN). Finally, we carried out a contrast experiment; two groups of students learned the same web-based textbook and took the same test, the result shows that the group using the navigation function outperforms the group who didn’t use the navigation function by about 6 percentage points. Keywords: Web-based learning, collaborative, individual-paced learning,group-paced learning, supporting system, navigation function

Introduction Computer and network technology now are playing a more and more important role in education. We have witnessed great successes as computer brings great convenience, high efficiency to the human learning activity. In the conventional WCLS research, the effort involved in the development of useful and powerful WCLS applications is only justified by whether the communication and collaboration or communication is well supported or not, whether they can be applied to a large number of learning situations or not and whether they can survive the evolution of functional requirements and technological changes or not. The importance of collaboration tends to be over-emphasized in most WCLS systems (such as Warendorf & Tan, 1997; Okazaki et al., 1996; Brusilovsky et al., 1996; Nakabayashi et al., 1997). We think that individual-paced learning process should be the center activity even in WCLS system, collaboration supporting should be no more than a complement to individual-paced learning, because we think that knowledge can only be acquired by the learner’s own mental efforts in individual learning process, any help from peers in collaboration process plays a less important role than the effort of oneself. Group-paced learning works efficiently only when it is integrated into individual-paced learning; group-paced learning alone can not support learning ideally well. In this paper, we refer web-based collaborative learning (WCL) as studying by discussion/communication over learners via text-based chat channel; we refer web-based individual learning (WIL) as studying the web-based textbook by self-conduct, without any interference/communication with peers. WCL and WIL are two different learning states in WCLS system, how to balance and integrate them in a WCLS system should be a big research problem. Based on the above opinions, in our system there are: 1. WIL process is the center process, the learner spends most of the learning time on this process; 2. WCL is only needed when one’s learning run into impasse in the WIL process; 3. necessary navigation

226

T. Chenyuan et al. / A Novel Web-Based Collaborative Learning Supporting System

information should be provided to learner so that one can switch freely between WIL process and WCL process. 1. System Overview Fig. 1 shows us the flow of learning process. The learner starts self-conduct learningfrom the first web page; after mastering the knowledge of that page, he/she will move on to the next page, repeats this action until the goalis reached. In the course of this individual learning, impasse always happens. Impasse here means the cases such as learner feels some content is too difficult to understand, or the learner is not very confident about what he/she has understood. Once the learner meets some impasse, it is the time for one to switch to WCL for help. Since all the learners use the same system to learn the same pages, it is possible that some other learners share the same impasse, some other learners have understood the content well; discussion witheach other should be an efficient way to look for answer. The learner can switch back to WIL from WCL at any time he/she wants.

The interface of the system is like the picture shown in Fig. 2. (a) is the textbook area; (b) is the navigation area, there are two navigation windows, one is Page Navigation, and the other is Discussion Navigation; (c) is the knowledge point list of the textbook; one page corresponds to one item in the knowledge point list; (d) is the text-based chat window; (e) is message dialogue. 2. Navigation Function Just web page textbook and communication channel obviously are not enough to serve our purpose. To make the WIL be the main learning process, three kinds of information should be presented to each learner: 1. the page-reading progress of every learner, which means, at the time being, to which page learners have read. 2. The discussion group should be categorized according to the topic of the group, which means discussion group is categorized corresponding to web pages. 3. The information of the current discussion groups, that means, at the time being, the information, such as who and who are discussing what. Therefore, we designed the navigation function: a. Page Navigation The page navigation is designed as shown in the left of Fig. 3. It is a graph window visualizing at some time point all the online learners’ reading progress. The vertical line represents the ID of learners, and the horizontal line represents the ID of textbook page. For example, in the left of Fig. 3, the learner 1_01 has finished reading page “߳”. b. Discussion Navigation By referring page navigation, learner can choose a person who is thought to be suitable to form a group. But if the learner chosen is in some other discussion at the time being, the quality of discussing with him/her will decrease in some sense. So, just knowing the page navigation is not enough, it is necessary to know all the ongoing states and actions of all the learners. As a measure, we proposed the discussion navigation, which is a graph window

T. Chenyuan et al. / A Novel Web-Based Collaborative Learning Supporting System

227

visualizing the information that who is in which group at the time being. The outlook is as shown in right of Fig. 3. The vertical line represents the ID of learners, and the horizontal line represents the ID of web page, which is the topic of the discussion group. As we can see from the right of Fig. 3, now learner [l_01] and learner [l_04] is in the discussion group of “ߦ”.

Fig. 3 page navigation and discussion navigation The two navigation windows have brought more advantage than we have expected. For example, it also makes the learner become aware of the existence of other learners’ progress to adjust the learning speed. It also contributes to the enthusiasm of participating in discussion. Moreover, by teaching each other, the learners are expected to be aware of various problems in their knowledge comprehension, it makes the learner learn the web pages more positively and actively. 3. Experiments and Evaluation A Japanese language web-based textbook about particle was used in this experiment. The web-based textbook has about 20 pages. The participators of this experiment are 10 short-term students, who were randomly chosen. They are the beginners of Japanese language; although the content of the web-based textbook is new to them, it is exactly what they were going to learn. We randomly divided the students into two groups, each has 5 students: x A group: learn the web-based textbook in 90 minutes, using the system as illustrated in above sections. x B group: learn the web-based textbook in 90 minutes, using the system, communication over learners is allowed but the navigation functions are also removed from the system. After the studying finished, two groups were asked to take a same test, there are two questions in the test, the first one is filling 37 blanks in the sentences using particles; the second one is writing a composition according to a set of pictures. Table1 and table2 show us the test result of question1 and question2 respectively: the number of correct answers Individual percentage= × 100% the total number of questions(37) ¦ Individual percentage × 100% Group percentage= the number of participaters Group Leaner ID

A

B

1_01 1_02 1_03 1_04 1_05 1_06 1_07 1_08 1_09 1_10

Number of Correct Answer 33 20 30 24 27 27 28 26 14 28

Individual Percentage

Group Percentage

89.19 54.05 81.08 64.86 72.79 72.79 75.68 70.27 37.84 75.68

Table 1. The test result of question 1

72.39

66.45

228

T. Chenyuan et al. / A Novel Web-Based Collaborative Learning Supporting System

As to the second question, the total number of incorrect answer means how many mistakes in each learner’s article, including any mistake; the number of incorrect answer in particle represents how many mistakes in particle usage. The Group percentage is calculated using the following formulation: ¦ the number of incorrect particale × 100% Group percentage= ¦ the total number of incorrect answer Group Leaner ID

A

B

1_01 1_02 1_03 1_04 1_05 1_06 1_07 1_08 1_09 1_10

Total Number of Incorrect Answer 3 5 3 8 1 6 3 11 8 7

Number of Incorrect Answer in Particle 2 1 1 3 1 1 1 4 2 4

Group Percentage

40.00

34.28

Table 2. The test result of question 2 Statistical analysis of the result of the first questionˈshowed us that the group-A outperformed the group-B at Group Percentage by about 6 percentage points. That was the result which we hoped for. We want to say that the proposed navigation functions made this difference. The result gives us much confidence and encouragement. The result of the second question makes us depressed a little, because Group Percentage of group-A is greater than that of group-B by about 5 percentage points. As a possible explanation, we think that when one write a composition in a test, one always use the phrases about which one is most confident. So, maybe although group-B has less knowledge of particles than group-A(this is reflected in the result of question 1), if they only used the particles which they are very confident, it will not be a surprise to see that the Group Percentage of group-A is greater than that of group-B.If this explanation is right, the synthetic result of question 1 and question 2 proved that our proposed system is valid. 4. Conclusions

We think it is necessary to change the traditional tendency that in WCLS systems the individual-paced learning was not attached greater importance than group-paced learning. In our system, individual-paced learning becomes the center process, and collaborative learning environment becomes the slave process. A navigation function is introduced to make our idea become reality. It is composed of two parts, one is Page Navigation, and the other is Discussion Navigation. Finally, we did an experiment to evaluate the system; the experiment results give us much confidence and encouragementabout the validity of our proposition. We hope our proposed system and experiment data analysis will contribute to the future development of WCLS, and we are confident about that. References [1] Elliot, C. (1997). Implementing Web-based intelligent tutors. Paper presented at workshop “Adaptive Systems and User Modelling on the Web Wide Web”, 6th International Conference on User Modelling, 2-5 June 1997, Chia Laguna, Sardinia. [2] Calvert, H. M. (2000). Document delivery options for distance education students and electronic reserve service at Ball State University Libraries. In P. S. Thomas (Ed.), The Ninth Off-Campus Library Services Conference Proceedings (pp. 73-82). Mount Pleasant, MI: Central Michigan University. [3] Zuoliang, C., Shigeyoshi, W. (2005). An Instance Structure Design of Virtual E-Learning Community. Proceedings of International Conference Cognition and Exploratory Learning in Digital Age (pp. 387-390), December 2005.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

229

Towards Auto-Coding of Collaborative Interaction Texts Based on Maximum Entropy Approach Jian Liao, Ronghuai Huang, Yanyan Li, Jingjing Wang, Jing Leng Knowledge Science & Engineering Institute, Beijing Normal University, 100875, Beijing, China [email protected] Abstract˖Content analysis is an important method in the research into collaborative learning. The usual method for deeper content analysis is to first categorize and code interactive texts and then analyze them. However, the content analysis method with categorizing and coding is time consuming in the first period of research for coding content analysis, which restricts greatly the amount and scale of the analyzed contents. Therefore, this paper proposes a method to automatically code the collaborative interaction text based on the maximum entropy approach. Taken a great number of interactive notes as input corpus, an experiment is conducted to test the efficacy of the proposed approach. The experiment results show that the coding effect of the proposed auto-coding approach is FORVH to that of the sentence-opener approach in which the coding category is manually chosen by students. Additionally, as corpus increase and features are improved, the coding effects will be further strengthened. Keywords˖Content analysis; maximum entropy; auto-coding; CSCL

1 Introduction In CSCL domainˈContent analysis based on coding is applied extensively. Henri ˄1991˅is the first person who puts forward an analysis model of understanding interactive contents.. Besides,Zhu(1996), Pena-Shaff(2001), N.M.Avouris and other scholars analyze and research on the interaction contents in collaborative learning from different dimensions. Content analysis becomes a hot topic and an emphasis in collaborative learning research. However, the content analysis method with categorizing and coding is manpower-and-time consuming in the first period of research for coding content analysis, which restricts greatly the amount and scale of the analyzed contents. Therefore, how to use various computer technologies to auto-coding the analyzed content is a subject of great research value.

230

J. Liao et al. / Towards Auto-Coding of Collaborative Interaction Texts

2 Auto-Coding based on Maximum Entropy Approach 2.1 Introduction of maximum entropy approach The concept of maximum entropy have computers become powerful enough to permit the widescale application of this concept to real world problems in statistical estimation and pattern recognition. Since the 1990’s, it has been widely used in word tagging, word sense disambiguation, machine translation of natural language processing. Specific algorithm can be referred to Reference [6]. 2.2 Framework of ACME Model Auto-coding based on Maximum Entropy (ACME˅model flow is shown in Figure 1. First of all, some records that have been coded be coding specialists are taken as training corpus Then the system will evaluate the features extracted by these specialists. Those features that are kept by the specialist will be held in the feature base. Training Corpus

Feature

Feature

Feature

(Coded Text)

Pre Selection

Evaluation

Selection

Feature Base

Feature Extraction Append Corpus

Create

Feature

Model

Extraction

ME Model

Auto Coding

Coding Evaluation Test Corpus

Manual

Test Corpus

(Coded Text)

Correction

(Uncoded Text)

Figure1 ACME Model

Next, the system extracts all features in each record of training corpus according to the features in the feature base. Then the features extracted from each record and the manual coding of this record are taken as the training text of maximum entropy. The correlative argument of the maximum entropy approach is calculated and these arguments are saved in the maximum entropy approach base. The process of auto-coding begins with extracting the existing features in the feature base from each record of test corpus. After getting the features of each record, maximum entropy approach will figure out the probability of each record belonging to each sort of coding and the largest probability of coding will be taken as the coding result. Then, coding specialists can correct the results of auto-coding and the system will evaluate the results of auto-coding according to the correction. The results of judgment will help the specialists further optimize coding system. Finally, the test corpuses that have been coded by the specialists lately are added to training corpus in order to extend it. Therefore, a kind of cyclical machine learning process is formed. The reliability of system recognition will be promoted with the continuous extension of corpus and continuous perfection of features.

J. Liao et al. / Towards Auto-Coding of Collaborative Interaction Texts

231

2.3 Definition of Features The simplest definition of feature is to take keywords as the feature. Additionalˈwe can consider better feature definition, such as fuzzy match frequently used in inquiry in the database. That is we can add asterisk wildcard like *, ˛and so on, which will extent the scale of matching at feature matching. 2.4 Selection of Feature z

The coding specialists pre-select the features in each record according to their experience; then the machine carries out feature evaluation automatically. The criteria can be described as Credibility. The credibility is defined as follow. Credibility = Correct/All Where “Correct” represents the number of records of the coding represented by the feature as the coding result and also including the feature in corpus, “All” represents the number of all records containing the feature in corpus.

2.5 The Creation of ME Model and Auto-coding Maximum entropy approach gets a serial of coding arguments according to the number of features appearing in each coded record of training corpus and saves it in a maximum entropy approach model. Thus, we can use the argument in this base directly for auto-coding. 2.6 Evaluation of Coding Results The coding result is judged mainly by two criteria, which are precision and recall rate. Definition1.

Precision=

The number of correct auto coding The total number of auto coding

Definition2. Recall rate=

The number of correct auto coding The total number of manual coding

3 Experimental Study

Correlative experiment is carried out for verifying the model. .First of all, we collect 9240 interactive records in collaborative learning. Then we adopt a set of coding system especially designed to recognize students’ interactive intention in the interactive process to code these records. There are 28 codes in this coding system . Next, we take these 9240 interactive records as corpus and select the features that are able to code corresponding codes. The features can be described in features with asterisk wildcards like þ*ÿand þ˛ÿ, such as Ā*Bye-bye*āˈĀI think*?*āetc. Then we numerate the credibility of each feature, After that, we select new records of

232

J. Liao et al. / Towards Auto-Coding of Collaborative Interaction Texts

speeches in collaborative learning as test corpus for testing. As a comparison, we also use the opener-sentence system which adopts the same code system and corpus. The test results are shown as Table1. 

7HVW &RUSXV

)HDWXUHV

&RUUHFWLQ

6XPRI

$XWRFRGLQJ

$XWRFRGLQJ

6XPRI 0DQXDO 3UHFLVLRQ 5HFDOO &RGLQJ

2SHQHUVHQWHQFH















([SHULPHQW 















([SHULPHQW















([SHULPHQW















([SHULPHQW















([SHULPHQW 















Table1 Coding Result

In Experiment 1, we define all the records without coding by machine as a special coding. In Experiment 5, if the coding done by specialists appears in the first three places of the coding probability ranking of each record done by machine, we consider it to be successful coded.

4 Conclusion According to the experimental results, we can initially draw the following conclusions. First, though the performance of ACME still lower than the sentence-opener approach, it’s not obvious. Second, if quality and quantity of the corpus and features are improved, the result of auto-coding will be better. More researches will be carried out such as Taking regular express to describe features ,optimizing coding , refining the features, using a larger scale of corpus and so on.

References [1] Henri, P.(1991).Computer conferencing and content analysis .In Kaye,A.R(Ed.),Collaborative Learning through Computer Conferencing: The Najaden Papers.Berlin:Springer-Verlag,p117-136. [2] LIU Huang Lingzi; ZHU Lingli; CHEN Yiqin & HUANG Ronghuai Δ π A Research about Collaborative Knowledge Building through Interaction AnalysisρΔOpen Education Research Vol.11, No.2, P31-37, 2005. [3] Software for Content Analysis – A Review Will Lowe (2002)

http://people.iq.harvard.edu/~wlowe/Publications/rev.pdf [4] Wilson, T. (2001). Review of Atlas-ti. Information Research, 6(3). [5] Adam L.Berger

Stephen A.Della Pietra Vincent J.Della Pietra A Maximum Entropy Approach to

Natural Language Processing 1996 Association for Computational Linguistics [6] Stephen Della Pietra, Vincent Della Pietra, John Lafferty Inducing Features of Random Fields IEEE transaction pattern analysis and machine intelligence vol.19,NO.4 April 1997 [7] LI Su-Jian WANG Hou-Feng YU Shi-Wen XIN Cheng-Sheng

Research on Maximum Entropy

Model for Feature Indexing Chinese Journal of Computers Vol. 27 No.9 Sept. 2004

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

233

Web based Collaborative Environment for Engineering Graphics Education Shen Lianguan, Li MujunWang Xiaodong, Zhao Wei, Zheng J.J. Department of Precision Machinery and Precision Instrumentation, University of Science & Technology of China Hefei, Anhui 230026 China [email protected]

Abstract: A web based interactive environment specially for Engineering Graphics education is proposed, which provides a virtual collaborative education platform for geographically distributed teachers and students. The system of B/S (browser/server) mode, different from general netmeeting program, can make a vectorgraph of DWG format show on clients' browsers online synchronously. And then the graphics can be discussed by modifying the objects or putting designed marks on it, as if on a sheet of blueprint. The updated objects by a user can show on the others’ browsers immediately. The users can also talk and listen to each other to express their own opinions. In the system the users are organized in different groups according to respective interested topics. In a group there is a controller and the control power can be transferred among the members. The system serves as a bridge between students and teachers for their extracurricular activities. With the tool the student can give his/her haze on browser and ask for help or choose their desired teacher to give some advice, while the teacher can correct student’s schoolwork on their own browser. When the schoolwork is checked and corrected, no suspicion of its version is needed to have, the original drawing remains intact and the subsequent external marks are saved in different file respectively. Once the teacher wants to disabuse and give his instructions to one or some students he should only declare or inform them to come to the website at same time and no necessary to come to classroom. Now it has integrated together with a self developed Web based Engineering Graphics courseware and a praxis system into a complete web based engineering education system. Keywords: collaborative environment, Engineering Graphics, Web based CAD, virtual classroom Introduction In the movement of rapid development of manufacturing and information technology, the engineering education should be changed from the architectonic and systematic viewpoint. With the impetuous development of the multimedia and Internet technology in the 1990’s, the education reform has been brought its unprecedented opportunity. The curriculum of Engineering Graphics is one of the required and public elementary courses in the universities and colleges in China. The main goal of the course is to let students master the skills of drawing and reading engineering drawings. Its fundamentality and age-old characters have aroused our enthusiasm to give it new vigorous vitality. In consideration of the whole education chain we have proposed a series of measures. With the multimedia technology we have developed a package of the whole teaching material of Engineering Drawing courses and put it on the Web since 1998[1]. The courseware hammers at conversion between 3D model and 2D views and imitates the action of the teachers in class vividly, lively and accurately. So the students get the knowledge in a relaxed studying environment by audio-visual senses synchronously. As a result, a great ardour for studying curriculum has been excitated unexpectedly. The practice in the passed

234

L. Shen et al. / Web Based Collaborative Environment for Engineering Graphics Education

8 years has proved its favor for the raising of students’ quality. Fig. 1 is the image of one of the webpage with amicable man-machine interaction. As a matched component, we have constructed a Web based interactive praxis system. Its most notable features are Engineering drawing-based and web-based[2]. Fig.2 shows its Web Business logic Schema. Teaching practice has proved the mentioned measures are very effective for cutting the teaching time and enhancing teaching qualities. But some deficiencies have emerged out. Shortage of gesture language and direct communion between teachers and students face to face, especially, when the students have some suspicions or difficulties, has caused some puzzle dom. In the case, it is necessary to have an interactive communication platform to gather teacher and his (her) students and make teaching and learning synchronously and asynchronously possible. So we hope the extracurricular activities expanded collaboratively. And then an idea to construct a virtual classroom has bourgeoned. The kernel technology is to develop a web based CAD system (we name it Webcad).

Fig. 1. Architecture of the system Fig.2. Connection process

1. Fundamental aspects of the system 1.1. The architecture of the system To implement a reliable, fast-response and platform-independent cooperative system, Java Socket is used to construct the architecture for the distributed system. The system is mainly composed of three tiers: Browser/Server/Data, as shown in fig.1. On the client side, a web browser with an embedded applet provides the GUI (Graphical User Interface) for users. The Server side contains all the application logic and maintains the connections with clients, exchanges and manages the data from both database and the user workspace. In the 3-tier architecture mentioned above, the Java Database connectivity (JDBC) is used for the connection between the Data and the Server, while the Java Socket is used for the communication between the Client and the Server. The detailed connection process is described in the Fig. 2.When the user logins, a socket connection will be created. And if the connection is successful a new thread will be created in the Server, which will then take charge of all the data and transactions of the users. Then during the corporation work, when an action is performed by a user, this operation message will be sent to the Server, and then dispatched to all other users after processing. This enables the system to keep the data and operation synchronous among users.

L. Shen et al. / Web Based Collaborative Environment for Engineering Graphics Education

235

1. 2 The priority-based mechanism of collaborative control The collaborative discussing team is comprised of polyonymous participants. So it is necessary to establish an access control mechanism to resolve conflict occurred during the collaborative drawing. In the system, the server maintains a sequence with the objects created by the users. Besides, a simple but practical and effective access control model is adopted[3]. When an operation happens to an object, there are three possible cases, as described in the Fig. 3. A) When a user (user_1) manipulates his own objects, he has the priority (named Menu bar

Tool bar

Created by User_1

A)

User_1

Action Obj_A

Execution topic list

Created by User_1

B)

User_2

Action Obj_A

Request priority

granted Execution

user list

Not granted Us er_ A 3 ctio n

C) User_2 Action

Obj_A

Chat area

failed Compare The highest priority PriorityExecution

Notify all users

1 on User_ Acti

Fig .3 The priority-based mechanism

Text input field

Workspace for corporation design

Fig. 4 Overview of the interface

owner-priority) to perform the action. So the operation will be executed immediately. B) A user (user_2) has no priority to manipulate the objects created by others. When user_2 requests to operate the objects created by user_1, a message should be sent to the user who creates the object, and ask for the priority to perform the action. If the request is approved, user_2 will get the priority (named granted-priority). The operation will be executed and after that user_1 will lost the granted-priority. C) When concurrent manipulations from many users happen to one object, the server will compare the priorities of the users. Only the operation from the user who has the highest priority will be executed, and then a message will be sent to all users. The strategy to compare the priority in WebCAD is, 1) the owner-priority takes precedence of all granted-priority, and 2) the granted-priority is characterized by time order, and the previous granted-priority takes precedence of the later one. 2 Multi-functional system Fig. 4 shows the web page of the multifunctional webCAD[4]. No software needs to be installed in the client side. To enhance the facilities a number of tools are provided for the users. (A) Drawing tools General drawing functions necessary for creating draft are developed in the WebCAD, just like line, circle, arc, ellipse, NURBS, text annotation and dimension annotation, and so on. The results are stored in common format (DWG). It is very important to be compatible with the commercial CAD software (like AutoCAD). (B) Browse tools The functions provide a precondition for collaborative discussing. These functions are designed to load DWG format files from the database of the system or from any hyperlinked

236

L. Shen et al. / Web Based Collaborative Environment for Engineering Graphics Education

URL, and then to operate the drafts. The people gathered in a group can browse a same draft at the same time. Different groups can load different drafts. (C) Red-mark tools Red-mark tools are used to mark on drafts. The function is developed for teachers to correct the homework and for people to discuss and evaluate the pattern. A mechanism is developed to save the mark-up objects in SVG format[5] [6] and store the original draft in formats of DWG. In the way, the homework of the students will be kept in their original version. SVG (Scalable Vector Graphics) is a new XML-based (Extensible Markup Language) graphics standard from the W3C that will enable Web documents to be smaller, faster and more interactive. (D) Multimedia communicating means. Users can also speak or write to each other by using the tool provided by the WebCAD. This tool has been fulfilled based on the Java Multimedia Framework (JMF). Talker or writer is exclusive and the others are listeners or readers. To express their opinions vividly, the users can use the image tool to import images. Currently the support image file formats include BMP, jpg and Gif. 3. Conclusions

This paper introduces our research work on developing a web-based synchronous cooperative environment for Engineering Graphics Education. Based on Java technology, a 3-tier architecture is proposed. A number of tools (such as the browse tools, the red-mark tools, the communicate tools and so on) are provided to make it a multifunctional system. In the system users can view and modify CAD files online, and discuss their opinions conveniently and efficiently with one another. SVG, which is a XML language for rich graphical content, is employed to store the collaborative discuss results for its advantage of platform-independence, small size, and standardization. At the same time, the concurrency problems (for example, the access control and the version control) are also designed and solved to reduce the collision in the distributed synchronous cooperative design. The system has been applied to the Engineering drawing education in USTC, and proved to be an effective way. Future issues include the improvement of usability of the system, like the tolerance auto-generation module, the 3D module and so on. Acknowledgments

This project is financially supported by the Education Reform Fund from Anhui province and our university USTC. We also thank the people, Li, Shufang, Wang, Hongwei, Li Liyu and the like, who participate in the project in the passed 8 years. References [1]. Shen, LG et al.(2001)Development and Practice of web Based Course of Mechanicel Drawing. Proceedings of IceCE, Xi’an, 16-18. [2]. Shen, LG and Wang, RF (2004) Research and Reform of the Teaching of the Course in Mechanical Drafting. Jiaoyu yu xiandaihua/ Chinese Journal of Education and modernization, v73, 34-40. [3]. Shen, LG and Li, MJ, et al.(2006) Web based cooperative virtual product design environment shared by designers and customers. Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg, v3865, 384-393. [4]. Zhou, Zq and Shen, LG, et al.(2005) Research of customers oriented virtual design environment. Jixie Gongcheng Xuebao/Chinese Journal of Mechanical Engineering, v 41, n 6, June, 137-142. [5]. http://www.w3.org/XML/ [6]. http://www.w3.org/TR/SVG/intro.html

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

237

Time-based Self-learning Support Using Collaborative Learning Process Masahide Kakehi+, Tomoko Kojiri++ and Toyohide Watanabe+ Department of Systems and Social Informatics, Nagoya University, Japan ++ Information Technology Center, Nagoya University, Japan {kakehi, kojiri, watanabe}@watanabe.ss.is.nagoya-u.ac.jp

+

Abstract: During the self-learning, learners tend to review the interaction log of collaborative learning of the similar exercises which they have already solved. However, because some utterances are not important, learners take a lot of time and work to review all utterances. Additionally, learners cannot always take notice of effective utterances. In order to cope with such situation, our objective is to construct a system which extracts effective utterances from learners’ viewpoints and provides the collaborative learning log with effective utterances highlighted. The effective utterances for learners change according to interval time between collaborative learning and self-learning. In this paper, experimental results for investigating effective utterances based on the interval time from the collaborative learning are described. Keywords: Self-learning, collaborative learning log, effective utterances, learner’s viewpoint, interval time between collaborative learning and self-learning

1. Introduction Learners communicate with other learners through the network in the collaborative learning environment. During the learning, learners utter what they understood and what they thought. So, it is useful for learners to review the interaction log when they tackle similar type of exercise in self-learning. However, it is difficult for learners to find utterances that help them to solve the exercise from the interaction log because there contain a huge amount of utterances. Currently, we have constructed a system which detects effective utterances for each learner and provides the collaborative learning log with effective utterances highlighted [1,2]. This system focuses on exercises that contain several answering paths, such as programming. Effective utterances for learners are classified into two types: utterances that of they think effective during learning, and effective utterances which they cannot notice of during the learning. When learners try to derive the answer by the same answering path which they derived during the collaborative learning, they recall the former type of utterances. On the other hand, when they want to discover new ideas, the latter type of utterances is important. Therefore, in our approach, utterances are classified into four groups according to annotations that are added by learners during the collaborative learning: utterances which learners recognized as the same viewpoint, utterances which learners thought different viewpoint, utterances of the same viewpoint which learners did not notice, and utterances of the different viewpoint which learners did not notice. However, the types of utterances that learners require may change according to the interval time between collaborative learning and self-learning. In order to investigate characteristics of utterances needed for each moment, experiments that compare effective utterances according to learners’ characteristics and interval time from collaborative learning are arried out. By providing new selection method in considering the interval time and learners’ characteristics, our system can provide more effective utterances.

238

M. Kakehi et al. / Time-Based Self-Learning Support Using Collaborative Learning Process

2. System for providing effective utterances Our system detects effective utterances for each learner and provides the collaborative learning log with effective utterances highlighted [1,2]. Since effective utterances for learners are different, two kinds of annotations are prepared: the same viewpoint and different viewpoint. The annotation of the same viewpoint indicates that utterances are useful for a learner to derive an answer during the collaborative learning. The annotation of the different viewpoint means that utterances are not effective for deriving the answer by his answering path, but may be key utterances for deriving different answering paths. Difference between answering viewpoints of learners is determined by their annotations. When both learners attach annotations of the same viewpoint to the same utterance, their answering viewpoints are the same. If one learner adds an annotation of the same viewpoint and another attaches that of the different viewpoint, they are regarded to have the different viewpoints. However, if both learners add annotations of different viewpoints, whether their answering viewpoints are the same or not is not specified. Effective utterances that learners cannot notice during the learning can be established based on other learners’ answering viewpoints and their attached annotations. Table 1 indicates the meaning of utterances to a learner according to the viewpoint of learners who attach annotations. Utterances are classified into the same and different viewpoints on the basis of answering viewpoints of learners and annotations which learners adds annotation. Table 1: Viewpoint of candidate of effective utterances Annotation : Same Annotation : Different Learner’s viewpoint : Same Same viewpoint Different viewpoint Learner’s viewpoint : Different Different viewpoint ---------------------------Let us assume the situation shown in Figure 㧝. In this example, the learner A and the learner B have the same answering viewpoint, while the learner C is different. When A attaches the annotation of the same viewpoint to an utterance, the utterance is the same viewpoint for B, too. However, for C, the utterance provides an idea for the different method. Thus, in our system firstly, viewpoints of other learners are determined by comparing with their annotations. Then, based on the viewpoints of learners, annotated utterances are classified into the same or different viewpoints.

Figure 1: Viewpoint of utterances based on learners’ viewpoints 3. Experiment The objective of our experiment is to investigate the change of effective utterances of learners according to the interval time after the collaborative learning has been finished. Twenty examinees in our laboratory participated in this experiment. Firstly, they had formed four groups and had studied collaboratively. Then, they were divided into two groups. In the group 1, four examinees were asked to study the same exercise individually three months after the collaborative learning. In the group 2, three examinees did the same activity a few days after the collaborative learning. At the self-learning, the interaction log of the collaborative learning was provided. Examinees were asked to mark utterances that were used to solve the exercise in the self-learning.

M. Kakehi et al. / Time-Based Self-Learning Support Using Collaborative Learning Process

239

The ratio of marked utterances in the utterances that system extracted were 56.5% for group 1 and 83.3% for group 2. The experimental results indicate that utterances which our system extracted are effective for examinees of both groups. Especially, for examinees with short interval time, utterances which our system extracted were more effective. However, when the interval time is long, examinees may forget contents discussed in the collaborative learning process. For further analysis, in order to investigate the relations between learners’ characteristics and effective utterances, group 1 was focused and annotated utterances for individual examinees were analyzed. Table 2 shows ratios of their own utterances in those that they annotated. They show remarkable differences among students. Figures 2 and 3 represent the relations between examinees’ own utterances and the ratios of the same and different utterances that they had annotated. In Figure 2, the positive correlation can be seen, and a negative correlation can be seen in Figure 3. Table 2: Group 1 Ratio of annotation to own utterance Examinee A 83% Examinee B 25% Examinee C 0% Examinee D 75% Average 45.8% Figure 4 indicates the contributions of each examinee during the collaborative learning. Figure 4 represents the relations between ratio of examinee’s utterances in all annotated utterances and ratio of examinee’s annotations in all annotations. Examinee A added many annotations, while only a few utterances of him were annotated. Therefore, it is seemed that he did not to contribute to the learning. On the contrary, examinee D did not attach many annotations, while his utterances were added with a lot of annotations. Therefore, with the results of Figure 4, examinee D leaded the learning and other learners acquired knowledge from examinee D. According to the results of Table 2 and Figure 2, 3, and 4, utterances of the same viewpoint are effective for learners who did not attach many annotations and were added many annotations by other learners. On the other hand, it is effective for learners who attached many annotations and who were not added many annotations to present utterances of the different viewpoint.

Figure 2: Relation between learners’  Figure 3: Relation between learners’        utterances and effective utterances  utterances and effective utterances          of same viewpoint           of different viewpoint          

240

M. Kakehi et al. / Time-Based Self-Learning Support Using Collaborative Learning Process

Figure 4: Contributions of examinees for collaborative learning These facts mean that a learner who utters effective opinions in collaborative learning tends to refer utterances of the same viewpoint in self-learning. On the other hand, a learner who did not contribute to the collaborative learning is likely to select utterances of different viewpoints in self-learning. 4. Conclusion In this paper, the experiments were executed in order to investigate characteristics of effective utterances that learners require according to the interval time between collaborative learning and self-learning and learners’ contributions to the collaborative learning. Experimental results indicated that utterances extracted based on answering viewpoints are effective for learners who use interaction log of the collaborative learning after a long interval time. Moreover, it is proved that learners who lead the collaborative learning prefer to utterances of the same viewpoint and learners who did not utter the meaning opinions use the other learners' utterances and utterances of a different viewpoint. In our future work, we should consider the way to present effective utterances to learners according to the experimental results. Furthermore, we try to construct a system which supports learners who do not participate in collaborative learning to study the same exercises in self-learning according to the collaborative learning log of other learners who study collaboratively.  Acknowledgments The authors would like to thank the 21st Century COE (Center of Excellence) Program for 2002, a project titled Intelligent Media (Speech and Images) Integration for Social Information Infrastructure, proposed by Nagoya University. References [1] M. Kakehi, T. Kojiri, and T. Watanabe: Annotation Interpretation of Collaborative Learning History for Self-Learning, Proc. of KES2006 [to be published in October 2006] [2] T. Kojiri, Y. Ogawa and T. Watanabe: Agent-oriented Support Environment in Web-based Collaborative Learning, Journal of Universal Computer Science, Vol. 7, Issue 3, (2001), 226-239.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

241

The Design of a Collaborative Learning Environment in a Mobile Technology Supported Classroom: Concept of Fraction Equivalence Siu Cheung KONG Department of Mathematics, Science, Social Sciences & Technology, The Hong Kong Institute of Education, Hong Kong [email protected] Abstract. Knowledge of fraction equivalence is fundamental for building knowledge of fractions. This study aims to design a collaborative learning environment for developing the concept of fraction equivalence in classroom settings. According to the cognitive conflict theory, the key for developing a concept is to arouse a strong interest or an appropriate level of anxiety in learners in recognizing the anomaly clearly and reappraising the cognitive conflict situation deeply. A mobile learning environment for collaborative engagement is created in this study to encourage the resolution of cognitive conflict. Two situations are designed to stimulate reflection of learners about the concept of fraction equivalence: one is triggered by the anomaly between the learning peers and the other is triggered by the anomaly between the learner and the computer system, which has the authority of correctness in the situation. Two pedagogical tools for encouraging reciprocal tutoring in the collaborative learning environment are elaborated. Keywords: cognitive conflict, collaborative learning, fraction equivalence, mobile learning

1. Introduction Collaborative learning is a process that encourages learners to participate in the coordinated and synchronous learning activities with a number of other learners [1]. It emphasizes the concept “every learner learns from everyone else” [2, p215]. This learning process provides learners with the opportunity to make contributions and appreciate the contributions of others. There are four characteristics of collaborative learning: sharing knowledge among peers, sharing learning authority, mediation from teachers, and heterogeneous grouping of learners [3]. In collaborative learning, learners take the role of knowledge provider in sharing their own knowledge and learning strategies with other group members. Learners can be a source of knowledge, which is traditionally regarded as the figure of learning authority in the learning process. The sharing of knowledge among teachers and learners thus leads to the sharing of learning authority among teachers and learners. Learners are empowered with learning control to a greater extent in collaborative learning. Teachers play the role of facilitator in collaborative learning to provide mediation for learning in groups, such as the adjustment of information flow and interaction among groups and group members. The heterogeneous grouping of learners is important in collaborative learning. This grouping strategy allows reciprocal tutoring and knowledge exchange among learners with diverse perspectives, experiences, and backgrounds.

242

S.C. Kong / The Design of a Collaborative Learning Environment

Collaborative learning is good for learners to develop knowledge and interpersonal communication skills. However, two obstacles decrease the effectiveness of the learning process: problems in class control during the active participation of learners and the unsatisfactory participation of particular quiet learners [4]. Mobile learning, an emergent learning approach, can address these two obstacles of collaborative learning. Mobile learning refers to the use of mobile technologies for learning and teaching. It is characterized as learning across space, time, topics, and technologies [5]. Mobile learning can assist collaborative activities across space and time, and can be used in traditional classrooms across topics and technologies. There are three attributes of existing mobile technologies that facilitate the design of collaborative learning activities in classroom settings. Firstly, the visualization capability of mobile devices enables learners to distribute cognitive loading to visualization tools. Learners are thus able to engage in activities for deep learning of subject matter that requires visualization support [6]. With the use of mobile devices, abstract concepts can be visualized and manipulated through visual representations. Learners may share and communicate their ideas and knowledge with visual support in collaborative learning activities. Secondly, most mobile devices are compatible with desktop computers. Software can be installed in mobile devices to perform required computation tasks. With the installation of appropriate software codes, such as coding for data-mining support in mobile devices, teachers are able to trace the progress and learning behavior of learners during learning tasks [6]. This also implies that with appropriate software design, teachers can make use of wireless-networked mobile devices to facilitate class control in collaborative learning activities, such as organizing and maintaining learners in heterogeneous groups and supervising the engagement of learners in collaborative tasks by generating questions with appropriate difficulties. Thirdly, the anonymity in the communication with the use of mobile devices helps to increase the participation of learners in collaborative learning activities by allowing them to express their ideas without revealing their identities to the class. The mobile device enables learners to privately work, reflect, and explore learning materials without others observing the development of their work. Learners can share the final product of their work with the class when they feel it has reached a satisfactory level. This dynamic combination of private work and public sharing may enhance learning in traditional classrooms [7]. Even quiet learners can share in the immense satisfaction of participating and contributing ideas without the fear of making incorrect responses.

2. Cognitive Conflict Understanding mathematical ideas often involves the restructuring of mathematical schema of learners. This restructuring process is intricately linked with the occurrence of cognitive conflict [8]. Cognitive conflict is a part of the psychological theories of cognitive change. It involves an inferred state of incompatibility between two inferred component states within the cognitive process [9]. In general, cognitive conflict is a perceptual state in which one notices the discrepancy between an anomalous situation and a preconception [10]. Since the 1980s, cognitive conflict has been regarded as a feasible teaching strategy because its nature of cognitive change induces introspection of learners towards the newly learnt conception that is incongruous with a preconception or existing misconception. According to the cognitive conflict process model, there are three stages in the engagement of cognitive conflict in learning [10]. The first stage is the preliminary stage,

S.C. Kong / The Design of a Collaborative Learning Environment

243

in which an anomalous situation that is different from belief of learners of pre-existing conceptions is introduced. The second stage is the conflict stage, in which learners recognize and reappraise the anomalous situation with the expression of interest or anxiety in resolving the cognitive conflict. The third stage is the resolution stage, in which learners try to resolve cognitive conflict in any way that they can. Cognitive conflict has three types of potential: constructive, destructive, and meaningless potential. When learners clearly recognize an anomaly and reappraise a cognitive conflict situation deeply with the expression of strong interest or appropriates anxiety, the cognitive conflict has constructive potential. When learners do not recognize an anomaly or simply ignore it with an expression of bad feeling, such as frustration or rejection, the cognitive conflict is regarded as destructive. When learners recognize an anomaly but accept it passively without interest and cognitive reappraisal, the cognitive conflict shows meaningless potential. Early studies have shown that the induction of constructive cognitive conflict promotes positive outcomes in classroom learning [11]. The creation of constructive cognitive conflict in learning largely depends on interdependency among learners. Hence, the creation of constructive cognitive conflict is closely related to the collaborative learning process that provides learners with ample opportunity to learn from peer-discussion. The concept of fraction equivalence is regarded as one of the most difficult topics in mathematical learning because of its abstract nature [12]. Knowledge of fraction equivalence is fundamental for building knowledge of fraction operations, such as adding or subtracting fractions with unlike denominators [13]. The construction of this mathematical knowledge is often accompanied by the occurrence of cognitive conflict [12]. As research findings have indicated that technology supported graphical modeling helps with the learning of fractions [12, 13, 14, 15], this research aims to design a collaborative learning environment for developing the concept of fraction equivalence in a mobile technology supported classroom. A mobile learning environment for collaborative engagement is created in this study to encourage the resolution of cognitive conflict and reciprocal tutoring in classroom settings.

3. Design of the Mobile Learning Environment for Collaborative Engagement 3.1 Mobile Technology Supported Classroom Collaboration is a coordinated, synchronous activity through a continued attempt to construct and maintain a shared conception of a problem [1]. In this study a series of synchronous interactions in a mobile technology supported classroom is designed to encourage learners to engage in learning tasks, and a mobile platform is established for immediate interaction between learners in collaborative pairs. Figure 1 depicts the mobile technology supported classroom for learning the concept of fraction equivalence in pairs. The learning activity of fraction equivalence takes place in a wireless-networked classroom. The mobile device that is used in this learning activity is a pocket PC because of its portability and relatively large screen. The teacher and learners are provided with a pocket PC. The pocket PC of teacher is pre-installed with the interface for managing the pair grouping and the organizing of learning activities. The pocket PC of learners is pre-installed with a graphical tool for learning fraction equivalence. The learners interact in pairs through a server that is connected to a SQL database. The server acts as a grouping coordinator of grouping requests from teachers and a communication coordinator of synchronous interactions between paired learners.

244

S.C. Kong / The Design of a Collaborative Learning Environment

Client Pocket PC

Server

Administrator Pocket PC

Student A Get

Communication Module

Post

Get

Grouping Module Post

Return Data Get Data Get Post

Student B

Teacher

Database

Record of Students

Figure 1: The mobile technology supported classroom for learning the concept of fraction equivalence

3.2 Pedagogical design for collaborative learning: Two situations for reflection Successful collaborative learning tasks should allow learners to achieve mutual engagement, negotiation, self-reflection, shared understanding, and mutual agreement [1]. The scale of grouping, the type of interaction, and the task of collaborative learning are critical factors for achieving these goals. To facilitate in-depth discussions among group members for developing the concept of fraction equivalence, learners are grouped into pairs with fellow classmates to conduct fraction comparison tasks. After logging onto the system, learners are placed in a context in which they decide the equivalence of two fractions with the support of graphical representations of the fractions. One learner is the designated question-setter, while the other is the question-replier. The learners alternate playing the two roles. There are three steps in this learning activity. Step 1 is the process of question-setting. In this step, the learners in the role of question-setter set and send out questions about the equivalence of two fraction expressions. The learners have to state whether the two fraction expressions that they set are equivalent to the aid of graphical representation of the two fraction expressions at the top of the interface. Once the learners are satisfied with the question set, they can click the “Confirm” button to send out the question to their partners through the server. Step 2 is the process of question-reply. In this step, the learners in the role of question-replier receive questions from their partners. When they receive the two fraction expressions, they have to decide on the equivalence of the two fraction expressions with the help of the visual representations of the fractions. After the learners have indicated their decisions, they click the “Confirm” button to send out their answers to the server. Step 3 is the process of judgment. In this step, the computer system plays the role of learning authority to assess the correctness of the questions that are set by the question-setters and the answers that are provided by the question-repliers. The computer system sends messages of “Correct” and “Incorrect” for the right and wrong questions or answers. There are two possible types of cognitive conflict that are engendered in the learning activity for the achievement of learning by reflection: one is triggered by the anomaly between the learning peers, and the other is triggered by the anomaly between the learner and the computer system. When one member of a pair of learners provides the correct question or answer, while another member gives the wrong question or answer, the computer system displays the message “Please Discuss” (see Figure 2a). This generates the first type of cognitive conflict ņ it invites learners to share understanding, and to engage in self-reflection and negotiation through collaborative interaction.

S.C. Kong / The Design of a Collaborative Learning Environment

Figure 2a: The computer system generates a message “Please Discuss” if group members have different views

245

Figure 2b: Each learner has to click the “Discussion Finished” button after the discussion

When both members agree to finish their discussion, they have to click the “Discussion Finished” button (see Figure 2b) to inform the computer system. The computer system then generates the message “Correct” or “Incorrect” for the question-setter and question-replier (see Figures 3a and 3b). These authority judgments create the second type of cognitive conflict when they differ from the judgments of the learners. This offers learners a second opportunity to engage in self-reflection and to share understanding through a post-task discussion.

Figure 3a: The computer system generates a message “Correct” for the right question set

Figure 3b: The computer system generates a message “Incorrect” for the wrong answer

4. Design for Resolution of Cognitive Conflicts 4.1 The status of groups of learners in learning the concept of fraction equivalence The aim of this study is to equip learners with basic knowledge of fraction equivalence through collaborative learning in a mobile technology supported classroom. It emphasizes the knowledge sharing of learners with graphical support in the learning process. The different learning progress of individual learners determines the learning progress of each

246

S.C. Kong / The Design of a Collaborative Learning Environment

group. Figure 4 depicts the status of groups of learners in the process of learning the concept of fraction equivalence. (Case 4) Student A -- 9 Student B -- 9 Learning Status 3 (Case 2)

(Case 3)

Student A -- 9 Student B -- 8

Student A -- 8 Student B -- 9

Learning Status 2

Learning Status 2 (Case 1) Student A -- 8 Student B -- 8 Learning Status 1

Figure 4: The status of groups of learners in the process of learning the concept of fraction equivalence

Case 1 is expected to occur commonly at the beginning of the learning process. In this case, both members of a group have a preconception or misconception about the equivalence of two fractions. The learners always set and reply to questions incorrectly. In this situation, learners always encounter both the first and second type of cognitive conflict. The groups of learners in this situation fall into learning status 1. Case 2 and Case 3 occur when one of the group members begins to grasp the concept of fraction equivalence better than his or her partner. The learner who has developed the concept of fraction equivalence begins to set correct questions and make responses to questions with correct answers, while his or her counterpart cannot always achieve this status. The learners in this situation have the first type of cognitive conflict and the learner who is developing the concept of fraction equivalence will continue to experience the second type of cognitive conflict, in which an anomalous situation exists between the learner and the judgment of the computer system. The groups of learners in these cases are in learning status 2. Case 4 occurs when both learners in a group have a good understanding of the concept of fraction equivalence. The group members always set and reply to questions correctly. In this case, cognitive conflict rarely occurs. The groups in this circumstance achieve learning status 3. This is the learning goal of all of the groups. Some groups may go through learning status 1 and 2 to reach learning status 3 and some of the groups may go directly from learning status 1 to 3. The groups in learning status 2 are heterogeneous groups in this study. 4.2 Design for encouraging reciprocal tutoring The ultimate goal of this design is to help all of the groups of learners to attain learning status 3 through the learning activities in the collaborative learning environment. In this regard, the groups in learning status 1 and 2 require the attention and mediation of teachers to promote productive knowledge sharing. In this collaborative learning environment, teachers play the role of mediator, rather than the authority on the judgment of correctness of equivalence of fractions. Hence, the role of the teacher is to encourage reciprocal tutoring, which can be achieved more productively in a heterogeneous group context and by promoting such tutoring activities in this group context. This may enhance the quality of arguments between group members and thus the induction of more constructive cognitive conflict. To realize this goal, two pedagogical tools are designed to encourage the reciprocal tutoring of learners. The first is the re-grouping of group members and the second is the changing of the modes of question-setting. Figure 5 shows the interface of

S.C. Kong / The Design of a Collaborative Learning Environment

247

the pocket PC of teacher for the re-grouping of learners and the changing of the mode of question-setting.

Figure 5: The interface of the pocket PC of teacher for the re-grouping of learners and the changing of the mode of question-setting

Teachers who observe groups that are working in learning status 1, in which both group members struggle with a concept over a period, can use the first pedagogical tool to swap a member from the group in learning status 1 with a member from a group in learning status 3. This helps to achieve more heterogeneous groups in the learning environment, which in turn helps to encourage prolific reciprocal tutoring. Teachers who detect groups that are working in learning status 2, in which one of the group members consistently designs incorrect questions, can use the second pedagogical tool to designate another learner as the sole question-setter by changing the mode of question-setting from “Turn-Taking” to “Designation”. This creates an environment that allows the learners with better understanding to tutor learners who are still developing the concept. Once it is speculated that all of the groups have attained learning status 3, in which the learners have a good understanding of the concept of fraction equivalence, teachers can change the mode of question-setting to “Random” for the entire class so that the role of question-setter is assigned randomly by the computer system. The “Random” question-setting mode provides the opportunity for learners to explore the concept further in a relaxed mode of inquiry. This helps to consolidate the learning outcomes of learners.

5. Conclusion This study aims to design a collaborative learning environment for developing the concept of fraction equivalence in mobile technology-supported classroom settings. In this study learners are asked to group into pairs and to join in a synchronous learning activity with the use of pocket PCs in a wireless-networked classroom environment. Two situations for the generation of cognitive conflict are designed to stimulate the reflection of learners to develop new concepts: one is triggered by the anomaly between the learning peers, while the other is triggered by the anomaly between the learner and the computer system that has the authority of correctness in the situation. Reciprocal tutoring is considered as the key strategy in this study that helps learners to resolve cognitive conflict. Thus, the role of the teacher in the classroom in this design is to act as a facilitator to mediate and to promote the sharing of knowledge and learning authority by helping learners to form heterogeneous

248

S.C. Kong / The Design of a Collaborative Learning Environment

groups and by adopting different modes of question-settings in these groups. The use of mobile technologies to encourage collaborative learning is a promising research direction that deserves research effort to study its effect on classroom learning environments. We have begun to study cases on learning the concept of fraction equivalence in this collaborative learning environment. Further work on large-scale studies in investigating whether learners recognize and reappraise the anomalies and how they attempt to resolve the cognitive conflict in the learning process will be attempted after the pilot case study. References [1]

[2]

[3]

[4] [5] [6]

[7] [8] [9]

[10]

[11] [12]

[14]

[14]

[15]

Roschelle, J., & Teasley, S.D. (1995). The construction of shared knowledge in collaborative problem solving. In C. O㧓Malley (Ed), Computer-Supported Collaborative Learning (pp. 145-168). Berlin: Springer-Verlag. Fischer, F., Bruhn, J., Gräsel, C., & Mandl, H. (2002). Fostering collaborative knowledge construction with visualization tools. Learning and Instruction, 12, 213-232. Dillenbourg, P. (1999). Introduction: what do you mean by “collaborative learning”? In P. Dillenbourg (Ed), Collaborative Learning: Cognitive and Computational Approaches (pp. 1-19). Amsterdam: Pergamon. Roschelle, J. (2003). Keynote paper: unlocking the learning value of wireless mobile devices. Journal of Computer Assisted Learning, 19, 260-272. Sharples, M., Taylor, J., & Vavoula, G. (2005). Towards a theory of mobile learning. Proceedings of mLearn 2005 Conference (9 pp.), Cape Town: mLearn 2005. Roschelle, J., & Pea, R. (2002). A walk on the WILD side: how wireless handhelds may change computer-supported collaborative learning. International Journal of Cognition and Technology, 1(1), 145-168. Vahey, P. and Crawford, V. (2003). Learning with handhelds: findings from classroom research. Menlo Park: SRI International. Tall, D. (1977). Conflicts and catastrophes in the learning of mathematics. Mathematical Education for Teaching, 2(4), 2-18. Cantor, G. N. (1983). Conflict, learning, and Piaget: comments on Zimmerman and Bloom㧓s “Toward an empirical test of the role of cognitive conflict in learning”. Developmental Review, 3, 39-53. Lee, G.H., Kwon, J.S., Park, S.S., Kim, J.W., Kwon, H.G., & Park, H.K. (2003). Development of an instrument for measuring cognitive conflict in secondary-level science classes. Journal of Research in Science Teaching, 40(6), 585-603. Limón, M. (2001). On the cognitive conflict as an instructional strategy for conceptual change: a critical appraisal. Learning and Instruction, 11, 357-380. Kong, S.C. and Kwok, L.F. (2002). Modeling a cognitive tool for teaching the addition/subtraction of common fractions. International Journal of Cognition and Technology, 1(2), 327-155. Kong, S. C. (2005). An experimental study on a cognitive tool for classroom use: a knowledge of fraction equivalence. In C.K.Looi, D. Jonassen, & M.Ikeda (Eds.), Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences (pp. 164-171). Amsterdam: IOS press. Kong, S.C. and Kwok, L.F. (2003). A graphical partitioning model for learning common fractions: designing affordances on a web-supported learning environment. Computers & Education, 40(2), 137-155. Kong, S.C., & Kwok, L.F. (2005). A cognitive tool for teaching the addition/subtraction of common fractions: a model of affordances. Computers & Education, 45(2), 245-265.

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

249

Scientific Modeling of Technology-Mediated Collaborative Learning Processes YEUNG Yau-yuen Department of Mathematics, Science, Social Sciences and Technology Hong Kong Institute of Education, CHINA HONG KONG [email protected] Abstract: Based on the well-established Monte Carlo approach called Ising model in magnetism, a concrete scientific model has been reformulated to simulate the students’ processes of collaborative learning as induced by different kinds of group interactions between students who learn under the same intelligent tutoring system and technology-mediated learning platform/environment. Furthermore, a package of computer programs for this model has been specifically developed to provide numerical solutions or predictions for some hypothetical or real educational contexts which are of significant interest to the certain educators. Keywords: Collaborative learning, Technology-mediated learning, Computer simulation

Introduction Over the last six decades after the invention of the first computer, scientific computation or computer simulation has been employed intensively and successfully to solve a lot of scientific and engineering problems and simulate the corresponding processes in a wide variety of physical systems as ranging from nuclear and atomic energy levels, weather forecasting, oil exploration, aero plane design to evolution of the Universe (see, e.g. Refs. [1-4]). In recent years, many theoretical physicists are engaged to apply the traditional theories in thermal/statistical physics to model the price fluctuation in international financial markets which generate some essential information for assessing and managing the risk associated with a portfolio of investments and to predict some critical stock market phenomena such as market crashes [5-6]. The models and methods of successfully simulating the financial sector are mostly adopted from those concepts and skills (e.g. cellular automata, Ising model and group renormalisation etc. in Refs.[2-4, 7]) of statistical physics and solid state physics which are normally employed to understand the dynamical behaviors of various complex adaptive systems in which the macroscopic behaviors or properties of those systems are controlled by their interacting microscopic constituents. They have also been applied to some other branches of social sciences such as the dynamics of social impact (or the so-called voter problem) and other behavioral processes in sociology [8-10]. Lewenstein, Nowak and Latane [9] showed that the dynamics of social impact can be quantitatively described in terms of a class of probabilistic cellular automata whereas Kacperski and Holyst [10] further applied the similar approach to model the formation of opinion in a society as arising from a strong leader and from an external social impact acting uniformly on all individuals. Their model successfully simulated the processes in the formation of two basic stationary states of opinion, namely cluster of the strong leader’s adherents and unification of opinions. Despite of those successful applications of computer simulation in many areas of social sciences, educators and

250

Y.-y. Yeung / Scientific Modeling of Technology-Mediated Collaborative Learning Processes

educational psychologists are traditionally quite skeptical and uncomfortable with the use of mathematical and computational techniques to describe education because they have argued that social situations are too complex to be formulated in a few equations [8]. Consequently, there is very few research work done on the scientific modeling of the educational processes. During the last two decades, researchers have started to employ various theories and techniques in cognitive neuroscience and artificial neural networks to model the students’ learning in certain subject disciplines (see, e.g. [11-12]). Since student model [13] which can record and analyze data on students’ present state of knowledge and personal attributes forms one of the four key components of an intelligent tutoring system (ITS), Stathacopoulou and his co-workers [14] have further applied neural network-based fuzzy model to evaluate various characteristics of students in an ITS with very satisfactory results. On the other hand, Virvou et al [15] developed an evaluation agent that can simulate students’ behaviour in an ITS. Recently, Bordogna and Albano [16-18] have taken a multidisciplinary approach to model the human-based teaching and learning processes in a classroom using the common concepts and methods used in sociology, educational psychology, statistical physics and computational science. In essence, they found a high degree of similarity between the physical interactions underlying the formation of magnetization in magnetic substances and students’ learning in many classroom environments which involve both the student-student collaborative interactions and teachers’ influence. (see Figure 1).

S

S

H Figure 1: Alignment of spins (the direction represented by arrows) in a piece of iron under an applied magnetic field H. Complete magnetization occurs when all spins align along with H.

They suggested that the teacher in a classroom is equivalent to an external magnetic field H applied to magnetize a piece of iron and that the academic achievement of every student in a class is analogous to the alignment of the spins (or so-called small magnets) in individual atoms. For a strong magnetization to be formed in the iron, the applied magnetic field must be strong enough to align all the individual spins in the same direction. Individual spins, being small magnets in themselves, can influence the alignment of their neighboring spins, causing them to align in a certain direction for the minimization of the total interaction energy. On the other hand, the students’ academic achievement depends not only on the ability (e.g. level of subject knowledge and persuasiveness of the teaching methods) of the teacher, but also on the student-student interactions usually in form of collaborative or cooperative learning processes occurring in a classroom context. Of course, there are some negative or unfavorable effects such as the idle chattering or lack of attention, which tend to hinder the students’ learning (or misalign the spins). To make

Y.-y. Yeung / Scientific Modeling of Technology-Mediated Collaborative Learning Processes

251

quantitative prediction on the proper trends of the student’s learning path, Bordogna and Albano assigned parameter values to different kinds of teacher-student interactions, student-student interactions and student self interactions. They found that their predictions closely matched with those data gathered in the classroom by educational psychologists, hence verifying the basic validity of their model.

Reformulation of the Ising model According to the Ising model [7] which has been widely employed by physicists to simulate the magnetization processes, whenever a misaligned spin S is formed (e.g. due to thermal excitation or “noise” effect) in a magnet, it will quickly re-orientate itself to match its neighbours. In a similar sense, a struggling student will catch up more quickly if he/she interacts intensively with a group of able students who possess higher level of cognitive and metacognitive learning ability. After critically reviewing the basic assumptions used in Bordogna and Albano’s approach [1-3], a direct analogy comparison between the magnetization in a piece of iron and the students’ learning in a technology-mediated collaborative learning environment is given as follows: No. 1. 2. 3. 4. 5.

Magnetism Technology-mediated collaborative learning Electron spin Sj of the jth atom jth student’s knowledge Sj(t) at time t A magnet with N atoms A class of N students Magnetization as measure of the strength of a Average academic achievement <S> of the whole magnet class External applied magnetic field H Intelligent tutor’s influence or interaction JT with each student Spin-spin interaction (with parameters Jij) which Student-student interaction with parameters Jij is equivalent to an internal magnetic field called and Mij Weiss molecular field HM(i) =

6.

¦ J ij S j

j zi The overall magnetization is determined by the The overall learning achievement is determined average of individual spin’s total energy by 3 types of cognitive and metacognitive impact J ij S j Si CiTS ( t ),CiSS ( t ),CiSO ( t ) Ei = -H Si - HM(i) Si =  HSi  j zi arising from the intelligent tutor-student, student-student and student-other interactions. The absolute temperature T (=1/E in units of the The 3 types of “noise effect” or called “learning Boltzmann’s constant) is a measure of the environment temperature” for learning, which include all kinds of student-independent “noise effect” contextual factors, are denoted as 1/ETS, 1/ESS and 1/ESO. ESS(N) has a dependency on the class size N.

¦

7.

After making the analogy comparison, the second step is to postulate/reformulate the most appropriate formulae for the 3 types of cognitive and metacognitive impact. Again if we follow the Ising model [7] approach as adopted by Refs. [16-18], some plausible forms of the cognitive and metacognitive impact are given below: (I) cognitive and metacognitive impact for intelligent tutor-student interactions CiTS ( t )

S  Si ( t ) J iT ( t )[ 1  Si ( t )ST ][ T ] 2

… (1)

in which Si(t) denotes the ith student’s prior knowledge (ranging from min.= -1 to max.=1) at the time t and ST represents the subject matter knowledge embedded in the intelligent tutoring system, normally having the fixed maximal value of 1 (unless its knowledge base contains erroneous or outdated pieces of knowledge). The coupling parameter JiT can be

252

Y.-y. Yeung / Scientific Modeling of Technology-Mediated Collaborative Learning Processes

written as a product of two terms: JiT(t) = Ji(t) ·JT in which the first term depends on the ith individual student’s cognitive and metacognitive abilities Ji(t) for learning with that particular ITS at time t. Value of the system-dependent factor JT is the same for all students and it depends on various educational aspects of the ITS such as (a) its instructional appropriateness and attractiveness of content presentation, (b) its effectiveness for probing the suitable questions and providing helpful hints, feedback and explanation for helping the students to overcome their learning difficulties, and (c) its ability for motivating, monitoring and assessing the students’ learning processes and for helping them develop appropriate learning strategies (i.e. metacognition). The present Eq.(1) is modified from Bordogna and Albano’s original approach [16-18] for human teachers by introducing the term in the second square bracket to account for situations of negative cognitive and metacognitive impact when the subject knowledge or instructional design of the intelligent tutoring system is very poor (e.g. ST < Si). Of course, some attributes of human teachers like teacher professionalism, teachers’ classroom management skills and teacher-student relationship could be omitted from the present model. (II) cognitive and metacognitive impact for student-student interactions CiSS ( t ) {

N

N

S j ( t )  Si ( t )

j 1, j zi

j 1, j z i

2

¦ J ij ( t )[ 1  Si ( t )S j ( t )]  ¦ M ij ( t )[ 1  Si ( t )S j ( t )] }[

]

… (2)

which arises from two kinds of student-student interactions, namely (i) impact of jth student’s persuasiveness Jij(t) on the ith student which becomes favorable when Si < 0 but Sj >0; (ii) impact of jth student’s mutual support Mij(t) on the ith student which becomes favorable when Si and Sj are both positive or both negative, i.e. affinity factor when both students have similar knowledge level, learning style, metacognitive ability and common interests/hobbies etc. This equation is again modified from Bordogna and Albano’s approach [16-18] by replacing their sign function with the last term outside the curly bracket to properly account for the effect of a poor learning ability student on an able student when they interact or carry out some learning activities together because the former approach has omitted all cases with Sj < Si. The effect of different design principles for technology-mediated collaborative learning environments will be multiplicatively absorbed in the parameters Jij(t) and Mij(t). (III) cognitive and metacognitive impact for student-other interactions CiSO (t )

Ai [1  S i (t )]

… (3) th

in which Ai is a parameter representing the combined effect of the i student’s (a) self-learning ability and strategy (i.e. metacognitive ability in learning) and (b) interactions with or learning from other materials or persons (e.g. reference books, CD-ROM, TV programs, museum visits, friends, parents or school teachers etc.) rather than the intelligent tutoring system or students/classmates inside the same technology-mediated learning platform or environment. This equation is simplified from Bordogna and Albano’s corresponding equation with a special consideration to combine those two variables (a) and (b) into a single variable Ai because they are practically inseparable in this computer modeling of learning processes. To find the evolution of learning of an individual student, we shall apply the well-known Monte Carlo method (see Refs.[2, 7, 19]) for the simulation of those spin-like complex systems. At a given time interval 't, the student’s learning may lead to either (i) a knowledge gain of amount 'S which is a small incremental change of knowledge consistent with the artificial neural network model of learning [11-12], i.e.

Y.-y. Yeung / Scientific Modeling of Technology-Mediated Collaborative Learning Processes

Si(t + 't) = Si(t) + 'S

… (4)

Wi

… (5)

with a probability of Pi or (ii)

253

1 Wi

no knowledge gain, i.e. Si(t + 't) = Si(t)

… (4’)

with a probability of (1 – Pi) TS

SS

… (5’) SO

… (6) where W i e ETS Ci ( t ) E SS Ci ( t ) E SO Ci ( t ) is the generalized Metropolis rate (see [4, 7, 19] for details). It is remarked that the knowledge loss case, i.e. Si(t + 't) = Si(t) - 'S as proposed in Bordogna and Albano’s original approach [16-18] is replaced by the present formula (4’) because the latter seems to be more consistent with science educators’ view on learning [11-12].

Simulated Results and Discussion To obtain the numerical solution of Eqs. (4) and (4’) for plotting the average learning path of different kinds of students, we need to develop a package of computer programs for solving the set of equations (1-6) altogether. This would involve some very complex and intensive programming jobs which should best be first developed using the MATLAB software (see e.g. Ref.[3]) which provides a lot of built-in matrix functions to greatly reduce to amount of programming work. However, we have rewritten the computer programs using the C language so as to substantially reduce the computing time and remove its dependence on the proprietary MATLAB software for concurrently running it in different computer systems like Linux and Unix. In the Windows platforms, a user-friendly interface for inputting and adjusting parameters has been constructed using the Visual Basic software and so it enables those researchers who lack the versatile programming skills to easily apply this package for carrying the numerical simulations of various educational contexts that they want to investigate in detail. This computer simulation package has undergone many cycles of testing and debugging processes to ensure its validity and correctness. To illustrate the key features of simulated results that could be obtained from our package of computer simulation programs, we have applied it to study the following two sets of research questions which are simple enough for setting the relevant input parameters and are of broad interest to many educators and ITS developers: 1. Assuming the same distribution of student prior knowledge and learning abilities, how does the student-student interaction affect the students’ average learning paths under a favorable or an unfavorable learning environment? 2. Under a favorable learning environment with the same distribution of student learning abilities, how does the student-student interaction affect the students’ average learning paths if they have different level of prior knowledge? Since we do not have an ITS and e-learning platform at hand for extracting the real-life values of the input parameters required in the above equations, it is necessary to set some approximate assumptions and typical values of the parameters for generating the simulated results to address the above two questions. First, we assume the 3 kinds of noise temperature to be roughly the same, i.e. ETS = ESS = ESO = E (say) and Jij(t) and Mij(t) are both directly proportional to the product of Si(t) and Sj(t). Second, for simplicity, we consider and

254

Y.-y. Yeung / Scientific Modeling of Technology-Mediated Collaborative Learning Processes

set JT =1 (i.e. perfect ITS design) and the proportionality constant for Jij(t) and Mij(t) to be both 1. Third, in order to focus on the effects of the intelligent tutor-student and student-student interactions as induced by the e-learning platform/environment, we omit the student-other interactions by setting Eq.(3) to be zero. Fourth, to be compatible with Bordogna and Albano’s previous calculations, we take the class size N = 96 which has been divided into 32 groups (with different settings) with 3 students in each group. 1

Students' average knowledge level

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0

50

100

150

200

250 time t 300

Figure 2: Effect of collaborative learning on students’ average learning path under a favorable (upper pair of curves with 1/E = 0.25) and an unfavorable (lower pair of curves with 1/E = 2.0) learning environments. Collaborative learning is included for the thicker (red) curves whereas the thinner (blue) curves contain no student-student interaction.

To address the research question #1, we further assume an uniform distribution over a range from -0.2 to +0.2 for the students’ prior knowledge, i.e. -0.2 ” Si(t=0) ” 0.2. The noise temperature 1/E is set to be 0.25 and 2.0 to mimic the favorable and unfavorable learning environments, respectively. We have run the simulation programs for 300 classes each with 1500 Monte Carlo simulation time steps and the smallest unit of knowledge gain 'S is set to be 0.05. In order to show more details of the initial phase of knowledge development, the average results for the students’ learning path (or development of knowledge) are given for the first 300 time steps only in Figure 2 which clearly shows that students generally learn faster with higher ultimate knowledge level under a more favorable learning environment (with smaller “noise temperature 1/E”). Collaborative learning does help to enhance the students’ learning outcomes in both kinds of learning environments but its effect is more profound for the less favorable learning environment. Under a very favorable learning environment (i.e. curves for 1/E = 0.25) in which the ITS design is very effective in facilitating students’ learning, the student-student interactions have a negative effect on the average students’ learning at the early stage of their development of knowledge. The reason is likely due to the fact that the learning progress of the students with better prior knowledge were slowed down by interacting with the students with poor prior knowledge but the effect is only short term and observable mainly in the very favorable learning environment. For the research question #2, we further assume a uniform distribution for the students’ prior knowledge in which the three ranges (group A) -0.7 ” Si(t=0) ” -0.3, (group B) -0.2 ” Si(t=0) ” 0.2, and (group C) 0.3 ” Si(t=0) ” 0.7 correspond to 3 groups of students having lower, intermediate and higher level of prior knowledge, respectively. The noise

Y.-y. Yeung / Scientific Modeling of Technology-Mediated Collaborative Learning Processes

255

temperature 1/E is set to be 0.25 which mimics the favorable learning environments. As shown in Figure 3 with Monte Carlo time steps truncated to 100 only for showing more details of the initial phase of knowledge development, all three groups of students in average will sooner or later follow essentially the same learning path with the same average amount of ultimate knowledge acquired. However, the group C students (with better prior knowledge) is initially disadvantaged by the collaborative learning activities because they may be confused by interacting with other students who are having poorer prior knowledge or holding misconceptions in certain subject matter. Furthermore, they may have to sacrifice their self-learning time for the early phase of collaborative learning activities which are educationally less beneficial to them. However, their knowledge level will quickly start to increase when the other two groups of students (especially group B) have acquired good enough knowledge. Besides, those group C students maybe able to develop better metacognitive learning abilities and gain a deeper level of understanding of the subject matter because they need to get aware of the different/alternative conceptions held by other students and to apply higher order thinking skills to help other students rectify or remove their alternative conceptions/misconceptions. On the other hand, the group A students (having the poorest prior knowledge) gain the greatest advantage through collaborative learning as the initial rate of their knowledge development (i.e. slope of the curve) is the highest amongst the three groups of students. 1

Students' knowledge level

0.8

0.6

0.4

0.2

0

-0.2

-0.4

-0.6

0

10

20

30

40

50

60

70

80

90

100 time t

Figure 3: Average knowledge development paths for 3 groups of collaborative learning students having (A) lower (blue crosses), (B) intermediate (red circles) and (C) higher (green triangles) levels of prior knowledge (at time t = 0).

Conclusion Based on the Ising model of magnetism, a scientific model has been reformulated to simulate the collaborative learning processes provided by any intelligent tutoring systems and e-learning platforms or environments. A package of computer programs have been successfully developed to obtain some characteristic curves for describing the students’ average learning path under certain situations of significant interest in education. For more accurate application of this scientific model, it will involve the fitting of the model

256

Y.-y. Yeung / Scientific Modeling of Technology-Mediated Collaborative Learning Processes

parameters to the actual collaborative learning outcomes of the students as recorded in the ITS and/or e-learning platforms. The fitted values of those model parameters could be input into the computer package for predicting the changes in the students’ path of knowledge development as induced by the changes in the design of the technology-mediated learning environment. Hence, they can provide some useful hints or qualitative guidance to the ITS or e-learning developers for finding possible/innovative ways to improve their system design [13-15]. Besides, this will also form an acid test on the precision and validity of this new scientific model on technology-mediated collaborative learning processes and to a great extent on the study of other complex and dynamical systems in social sciences [20]. Acknowledgements This work evolved from an earlier project funded by the HKIEd Research Committee. References [1] Yeung, Y.Y. and Rudowicz, C. (1992). Ligand Field Analysis of the 3dN ions at Orthorhombic or Higher Symmetry Sites. Computers and Chemistry, 16, 207-216. [2] Wong, S.S.M. (1997). Computational Methods in Physics and Engineering. Singapore: World Scientific. [3] van Loan, C.F. (2000). Introduction to Scientific Computing: A Matrix-Vector Approach Using MATLAB. Upper Saddle River, N.J.: Prentice-Hall. [4] Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. and Teller, E. (1953). Equation of state calculations by fast computing mechanics. Journal of Chemical Physics 21, 1087-1092. [5] Kertesz, J. and Kondor, I. (1998). Econophysics: An Emerging Science. Boston: Kluwer. [6] Chowdhury, D. and Stauffer, D. (1999). A generalized spin model of financial markets. European. Physical Journal B 8, 477-82. [7] Landau, D.P. and Binder, K. (2000). A Guide to Monte Carlo Simulations in Statistical Physics. Cambridge: Cambridge University Press. [8] Liebrand, W.B.G., Nowak, A., and Hegselmann, R. (Eds.)(1998). Computer Modeling of Social Processes. London: SAGE