Cognitive Load Factors in Instructional Design for Advanced Learners

COGNITIVE LOAD FACTORS IN INSTRUCTIONAL DESIGN FOR ADVANCED LEARNERS

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COGNITIVE LOAD FACTORS IN INSTRUCTIONAL DESIGN FOR ADVANCED LEARNERS

SLAVA KALYUGA

Nova Science Publishers, Inc. New York

Copyright © 2009 by Nova Science Publishers, Inc.

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CONTENTS Preface

vii

Chapter 1

Basic Architecture of Human Cognition

Chapter 2

Cognitive Studies of Expert-Novice Differences and Design of Instruction

21

Chapter 3

Cognitive Load Perspective in Instructional Design

35

Chapter 4

Cognitive Load Principles in Instructional Design for Advanced Learners

69

Toward a Cognitively Efficient Instructional Technology for Advanced Learners

91

Summary Index

1

99

PREFACE The empirical evidence described in this book indicates that instructional designs and procedures that are cognitively optimal for less knowledgeable learners may not be optimal for more advanced learners. Instructional designers or instructors need to evaluate accurately the learner levels of expertise to design or select optimal instructional procedures and formats. Frequently, learners need to be assessed in real time during an instructional session in order to adjust the design of further instruction appropriately. Traditional testing procedures may not be suitable for this purpose. The following chapters describe a cognitive load approach to the development of rapid schema-based tests of learner expertise. The proposed methods of cognitive diagnosis will be based on contemporary knowledge of human cognitive architecture and will be further used as means of optimizing cognitive load in learner-tailored computer-based learning environments.

Chapter 1

BASIC ARCHITECTURE OF HUMAN COGNITION A cognitive approach to human learning emphasizes the internal cognitive mechanisms of learning. Such mechanisms are usually described as transformations performed on various mental representations of situations and tasks. An important assumption of the approach is that a single general cognitive system underlies human cognition. Different theoretical approaches specify this general cognitive system as corresponding cognitive architectures. The understanding of human cognition within a cognitive architecture requires knowledge of corresponding models of memory organization, forms of knowledge representation, mechanisms of problem solving, and the nature of human expertise.

MEMORY ORGANIZATION The major characteristics of human memory are its strength or durability, capacity (number of items of information stored in memory), and speed of access. According to these characteristics, memory is divided into long-term memory and short-term memory. Long-term memory (LTM) is characterized by high strength and includes well-learned knowledge, for example, the name of the first US President, 5 x 5 = 25, or the spelling of the word potatoes. It is presumed to have unlimited capacity, although the access to the stored information could be slow. Both the strength of memory and the speed of access increase with practice. More

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fully elaborated and more deeply processed material results in better long-term memory. Short-term memory (STM), on the other hand, includes information that has been just encoded from sensory registers or retrieved from long- term memory, for example, what have you been thinking about just before this? what are you thinking about when dialing the phone number 8344 2124?. The durability of STM is a matter of seconds (Peterson & Peterson, 1959), and information in STM could be accessed very rapidly. The number of items of information that can be maintained in an active state simultaneously in STM is about seven units for most people (Miller, 1956). For example, it is very difficult for us to recall more than approximately seven serially presented random numbers (e.g., an unfamiliar phone number) a few seconds after we hear or see them, unless the numbers have been intentionally rehearsed. When asked to copy strings of digits from one page to another, we usually do this by grouping the digits by easily manageable units of three or four at a time. The most generally specified basic human cognitive architecture includes these two substructures (STM and LTM). Examples are the standard model (Newell & Simon, 1972) and modal model (Atkinson & Shiffrin, 1968; Waugh & Norman, 1965). In more specific models, these substructures might be regarded either as a single memory store with different modes of activation for long-term and short-term components, or as separate memory stores. These distinctions are not essential when considering the basic level of cognitive architecture. However, in order to explain human cognition, this general model needs to be supplemented by some attention control mechanism (central processor or central executive) which determines what information from sensory stores or LTM is brought into STM. The information that is actually attended to is limited to a small number of chunks in STM (Simon, 1979; Ericsson & Simon, 1993a, 1993b). Various cognitive architectures and elaborations of the general model extend the described memory structure. For example, the concept of working memory (WM) was introduced to account for processing of units of information that are interconnected, rather than random, and should be processed concurrently because of the nature of things they reflect or due to established associations in long-term memory. Working memory is considered as "a system for the temporary holding and manipulation of information during the performance of a range of cognitive tasks" (Baddeley, 1986, p. 34), a “desktop of the brain … that keeps track of what we are doing or where we are moment to moment, that holds information long enough to make a decision, to dial a telephone number, or to repeat a strange foreign word that we have just heard” (Logie, 1999, p.174). Some simple examples of working memory operation could be provided by the following tasks:

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close your eyes and pick up a pen in front of you; count the number of windows in your house or apartment; mentally rearrange the furniture in your room, or mentally complete a mathematical operation (for more examples, see Logie, 1999). After incoming stimuli from an external source are registered in sensory memory, perceived or matched to recognizable patterns by using prior knowledge (if any) in LTM and context, and are paid attention to, they are transferred into WM. If a unit of information is not recognized due to the lack of appropriate LTM patterns, it still could be attended to and processed in WM, with appropriate cognitive resources allocated for the task. Attended units of information in WM are assigned meaning and used for constructing integrated mental representations of a situation or task (Figure 1). This information, however, may fade very quickly if attention is diverted or if the capacity of WM is overloaded. Baddeley and Hitch (1974) first proposed that WM performed both processing and storage functions. They suggested three structural components of working memory: a central executive and two separate auditory and visual stores for handling verbal information and visual images. These two stores serve as maintenance systems controlled by the central executive and are called respectively an articulatory or phonological loop (‘inner voice’) and a visuospatial sketchpad (‘inner eye’). The limited capacity of the central executive is used for processing incoming information, with the remainder used for the storage of intermediate and final products of that processing. Storage and processing capabilities of WM trade off against each other. When memory load increases above some threshold, our performance could be inhibited. To get a feeling of WM limitations, try to mentally add two large numbers (for example, 83 468 437 and 93 849 040). For a concurrent task, you may try also to attend simultaneously to a comedy show on your TV. It would be very difficult to do because each of these activities alone may take all of your WM resources. There are three major functional aspects of working memory operation: temporary storage, manipulation of information, and executive control. Temporary storage of information was the focus of classic models of STM and was studied using standard word or digit STM span tasks. These were simple tasks involving recalling a list of digits or unrelated words and not requiring much prior knowledge. Active manipulation of information has been the focus of models of WM and has been studied using WM span tests that require concurrent processing of several tasks. These are relatively more complex tasks involving meaningful cognitive operations such as reading sentences or performing numerical transformations, and then recalling the final words of those sentences or results of the math operations. Performance of complex cognitive tasks requires

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simultaneous use and integration of various sources of information, coordination of separate processes and representations. It is the executive functioning of WM, interactions between WM and LTM knowledge structures that have become the focus of research in recent years (see Miyake & Shah, 1999, for a recent overview of WM models and the state of the field). A number of hypotheses have been proposed to explain individual differences in WM capacity and its relation to performance. These theories considered differences in total WM capacity, differences in processing efficiency of WM, or both. According to the total capacity approach (Baddeley & Hitch, 1974; Cantor & Engle, 1993; Case, 1985; Engle, Cantor, & Carullo, 1992), all cognitive processes require resources from a fixed pool. Any resources not allocated to the operations can be used for short-term storage. The storage and processing capabilities of working memory trade off against each other. When memory load increases above some threshold, a person’s performance may decline. A change in total capacity caused, for example, by fatigue or age should affect the performance in a wide range of tasks.

Working Memory Constructing mental representations of a situation or task

Long-Term Memory Knowledge base Sensory Memory: Incoming information Figure 1. Basic architecture of human cognition.

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The task-specific hypothesis (Daneman & Carpenter, 1980) assumed that WM capacity is specific to the particular task being performed. Efficient processing skills leave more WM capacity for storage of processing products. A change in processing efficiency should be specific to a particular task and result from intensive practice or training (Just & Carpenter, 1992). Performance would be influenced only if available resources are in short supply when a person operates at the limit of WM capacity. The processing efficiency approach assumes that a single central system is responsible for the processing and temporary storage of information. Its limited capacity must be shared between the processing and the storage demands. Individuals with inefficient processes have a functionally smaller storage capacity because they must allocate more resources to the processes (Daneman & Carpenter, 1983; Daneman & Tardif, 1987). Working memory capacity was measured in terms of operational capacity dependent on the type of specific background task used in a particular domain (Carpenter & Just, 1989). For example, the reading span test was used to measure WM capacity as the largest size of the set of simple sentences from which a subject can reliably recall the final words of all the sentences (Daneman & Carpenter, 1983). Daneman and Tardif (1987) established that the reading span was a measure specific to the language skills, not a measure of general working memory capacity, and it correlated significantly with reading comprehension ability. Although there obviously are systematic differences among individuals in their working memory capacity for specific tasks, and these differences influence performance when the person operates at the limit of his or her working memory capacity, no single approach or hypothesis concerning the interpretation of individual differences in WM capacity has received convincing empirical support. Such differences could be strongly influenced by knowledge structures available in long-term memory. Any WM span implicitly reflects an individual's knowledge and experience in a domain, and this knowledge inevitably influences his or her performance in both processing and storage parts of the task (e.g., Hulme, Maughan, & Brown, 1991; Hulme, Roodenrys, Brown, & Mercer, 1995). WM span measures thus could be used as predictors of the person’s performance in the corresponding domain rather than measures of his or her true general WM capacity. It is practically impossible to eliminate the influence of the person’s knowledge base when meaningful tasks are involved in WM span tests. From this point of view, approaches that focus on connections between the content and operation of working memory and long-term memory could be more relevant and productive.

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Simple chunking mechanisms provide an example of using long-term memory structures in transforming the content of working memory. The chunk is a familiar unit of information based upon previous learning. For example, it could be difficult to remember and recall a string of random letters like B,B,C,C,I,A,A,B,C,F,B,I, unless we chunk them together into BBC, CIA, ABC, FBI. In this case, we use our prior knowledge stored in LTM to reduce the number of elements to a manageable four chunks. The same method could be used with the following string of numbers: 1,9,1,4,1,9,4,5,1,9,9,6,2,0,0,1. Another common example of chunking in language comprehension is the way we chunk letters into familiar words, and words into familiar phrases. An STM capacity estimate of around seven units (Miller, 1956) actually indicates the number of chunks rather than total amount of information stored in STM. This mechanism explains how we manage to get around the information-processing bottleneck created by our limited working memory capacity, and to learn the enormous amount of knowledge in our LTM. People can be trained to effectively increase their memory capacity to an amazing degree through extensive training in chunking and re-chunking information into meaningful units using their prior knowledge stored in LTM. The skilled memory theory (Chase & Ericsson, 1982) claims that people develop mechanisms that enable them to use a large and familiar knowledge base to rapidly encode, store, and retrieve information within the area of their expertise and thus circumvent the working memory capacity limitations. As a result, experts possess an enhanced functional working memory capacity in domains of their expertise (Ericsson & Staszewski, 1989). Available domain-specific knowledge enables experts to quickly encode and retain large amounts of information in LTM. Such LTM storage and retrieval operations speed up with practice and are comparable with STM encoding and retrieval, resulting in experts' superior task performance and superior recall for familiar materials (the skilled memory effect; Ericsson & Staszewski, 1989). For example, expert mnemonists can increase their digit spans far beyond the limit of Miller's seven plus-or-minus two digits. They use familiar chunks of knowledge in LTM to encode new information in an easily accessible form. Ericsson and Staszewski (1989) described a person who expanded his digit span to 84 digits by grouping them into short sequences and encoding them in terms of, familiar to him, athletic running times, dates, and ages. He nevertheless operated under the constraints of limited-capacity STM: the size of digit groups never exceeded five digits, and these groups never were clustered in supergroups with more than four groups in a supergroup.

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In the WM model of Carpenter and Just (1989), the operation of WM during reading comprehension is also based on relations between WM and LTM. In this model, WM consists of currently active pointers to LTM structures and partial or final products of processing. A reader stores the theme of the text, the general representation of the situation, the major propositions from preceding sentences, as well as a representation of the sentence he or she is currently reading (Just & Carpenter, 1992). When dealing with an unstructured series of words, we can usually recall only six or seven unrelated words in order (according to our STM span). Skillful readers, on the other hand, can recall and understand long sentences (about 77% of words in up to 22-word sentences) because they use internal structures in LTM to circumvent WM limitations. Thus, sentence comprehension can be considered as recoding (chunking) incoming symbols into some structure (Carpenter & Just, 1989). Ericsson and Kintsch (1995) further developed these ideas into the theory of long-term working memory (LT-WM). In this theory, LTM knowledge structures associated with components of working memory form a LT-WM structure that is capable of holding virtually unlimited amount of information. Some additional mechanisms were introduced for overcoming the effects of interference in experts' use of LTM knowledge for storage and retrieval of newly encoded information were introduced. The proposed mechanism of LT-WM operation involves cuebased retrieval of information from LTM. The encoding method can be based on a specifically constructed retrieval structure, an elaborated existing memory structure, or a combination of the two. Skilled performance depends on domainspecific knowledge structures relevant to particular tasks, and, consequently, there are individual differences in the operation of LT-WM for a given task (Ericsson & Kintsch, 1995).

KNOWLEDGE REPRESENTATIONS Our knowledge base in LTM profoundly influences cognitive processes in most situations. Therefore, forms of knowledge representations are critical for understanding human cognition. Several major ways of representing the meaning of information in memory have been suggested: propositional representations (semantic networks), procedural representations (production systems), and schemas. Analogical representations or mental models (Rumelhart & Norman, 1983) can be generally considered as schemas. The concept of a proposition denotes the primitive unit of meaning, or a smallest unit of knowledge about which it is possible to make the judgment, true or false. Networks of such

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interconnected units can be used to represent the meaning of sentences and pictures. Newell and Simon (1972) suggested that knowledge could be represented by a set of conditional rules or productions condition → action. The production rules are stored in long-term memory and are retrieved and used in working memory. The current contents of working memory are matched against the conditions of all the production rules in long-term memory. Whenever the conditions of a rule occur in working memory, the rule is triggered and its action is carried out. Action of the rule can change the contents of working memory and determine which rule is triggered next. Thus, the principles determining how one rule is followed by another are built into the rules themselves. One of the most advanced theories based on the idea of production rules, the ACT* theory (Adaptive Control of Thought; Anderson, 1983), or its updated version ACT-R (R for rational; Anderson, 1993), suggest a separate type of longterm memory for production rules (for skills) in addition to the declarative memory (propositions, images, and other representations for facts and experiences). The items in these memories can vary in their degree of ‘activity’. If the contents of working memory match more than one rule in procedural memory then whichever is the most active is triggered. The concept of a schema, originally discussed by Bartlett (1932), came into cognitive psychology from research in artificial intelligence (Minsky, 1975; Bobrow & Winograd, 1977). Schemas generally represent the object as a set of attributes (slots). Schemas abstract generalizations about objects from specific instances, encode general categories and typical features. They may include not only propositions, but also perceptual features (for example, spatial images) and stereotypic sequences of events. Schemas may have slots with fixed or variable values; slots with variable values usually have some default or most probable values. The most important features of schemas are stable patterns of relationships between variables (slots). Each schema contains information about some class of structures. When particular values are assigned to slots of a schema, a schemabased knowledge structure could be obtained in the form of concepts, propositions, etc. The obtained knowledge structures could be more general or more specific depending on those values. Multiple schemas can be linked together and organized into sophisticated hierarchical structures where one schema can form part of a more complex schema. Schemas may represent knowledge of all kinds and levels: from individual letters (allowing us to recognize different variations of handwritten letters) to complex electronic or organizational systems, behavioral patterns, visual and

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auditory perceptual images. For example, our schema for a human face includes slots for eyes, a nose, a mouth, ears, etc. These components are arranged in a certain configuration that is not a rigid one. However, some general requirements should be met: the nose and eyes should be located above the mouth; eyes should be located above the nose on different sides of it, etc. This general schema allows us to recognize instances of human faces in limitless situations, including some peculiar forms of visual arts. A student’s schema for solving linear algebraic equations of the type ax = b may include three slots: 1) a number b on the right hand side of the equation; 2) a number a on the left hand side of the equation; and 3) the division operation: divide the content of the first slot on the content of the second slot. For less experienced students, the schema may include the operation of dividing both sides of the equation on the same number a. In this case, the schema would contain slots for both parts of the equation, the dividing number a, and the division operation. For an example of higher-level schematic knowledge representations, consider the technical domain that includes knowledge about various technical objects (e.g., tools, devices, machines, technological procedures). This variety of knowledge in any technical area could be represented with different levels of specification: from descriptions of general features to specific details. A schematic framework for representing knowledge about a technical object may include three main interconnected components that could be referred to as functional, operational, and structural descriptions. Any technical object could be characterized by some functions or purpose it was designed for (what is this object for?), processes utilized in the object’s operation (how does it operate?), and the object’s internal structure including links between its components (what does it consist of?). To explain an object’s operation means to explain why a given set of linked parts performs specific functions utilizing certain processes during operation. A learner should establish connections between functional, operational, and structural components of the object’s description in order to understand how it works (Kalyuga, 1984; 1990). Gruber and Russell (1996) suggested similar classes of an artifact description: structure (the physical and/or logical composition of an artifact in terms of the composition of parts and connection topologies), behavior (something an artifact might do in terms of observable states or changes), function (effect or goal to achieve by artifact behavior), requirements (prescriptions concerning the structure, behavior, and/or function that the artifact must satisfy), and objectives (specifications of desired properties of the artifact other than pure functions, such

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as cost and reliability). Requirements and objectives could be generally included into the functional description (as functional requirements and general functions).

functions of the object

alternative combinations of processes realizing a set of functions

alternative technical solutions realizing a combination of processes

Figure 2. General schematic structure of technical knowledge.

Each of above aspects of technical knowledge may have different levels of generalization. It is possible to describe an object in very general terms (a global level or general overview) or in more details with different levels of specification. When combined together, all aspects, components, and levels of the description of a technical object create a sophisticated multilevel hierarchical schematic structure of technical knowledge. In an abstract form, this structure could be represented by the graph in Figure 2. Three levels of description are shown for

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functions, processes, and structural components of a technical object. Simple and superficial knowledge about the object may include only isolated components corresponding to the upper rows in the depicted clusters of knowledge elements. Further deepening of knowledge requires establishing relations between these components and adding elaborated knowledge on more specific levels of description. There are many definitions of schemas depending on the theoretical perspective of the researcher. It is practically impossible to precisely describe the schematic knowledge structures held by an individual. As Norman (1983) noted, "we must … discard our hopes of finding neat, elegant mental models, but instead learn to understand the messy, sloppy, incomplete, and indistinct structures that people actually have" (p. 14). In general, a schema can be described functionally as a cognitive construct (an organized knowledge structure) that allows people to classify information according to the manner in which it will be used (e.g., Chi, Glaser, & Rees, 1982; Sweller, 1993). Such organized knowledge structures represent a major mechanism for extracting meaning from new information, acquiring and storing knowledge, circumventing the limitations of working memory, increasing the strength of memory, and recalling information. They impose an organization on the information, guide retrieval, and provide connections to prior knowledge. In schema theory, the process of learning can be considered as encoding new information in terms of existing schemas, as schema modification, or as the creation of new schemas. The creation or modification of a schema is based on conscious cognitive processing of information in working memory. In a more general context, schema acquisition could be regarded as an example of a nonlinear process where the schema emerges from lower-level components during learning or practice. As a cognitive unit, the schema represents a higher level of organization than just a simple collection of lower-level components. The need for the emergence of higher levels of schema hierarchy could be associated with general limitations of human information processing. In a wider context, any qualitatively new level of a system emerges in a non-linear way as a means to overcome the combinatorial barrier caused by immense number of possible combinations of the variety of elements of the previous, lower level. Examples of such processes are the emergence of the molecular level from atoms, biochemical structures from molecules, or nerve impulses from biochemical structures (Scott, 1995; Turchin, 1977). Structured neuronal groups might represent the qualitatively new biological level of conscious cognitive functioning (Edelman, 1992). On the psychological level of description, our abstract highlevel schematic knowledge representations in long-term memory (and

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corresponding intellectual abilities associated with operating such structures) might have emerged as a means of overcoming the combinatorial barrier under conditions of limited processing capacity. Because a schema is treated as a single unit in working memory, such highlevel structures require less working memory capacity for processing than the multiple, lower-level elements they contain, making the working memory load more manageable. Our abilities to construct and use higher-order hierarchical cognitive configurations of knowledge structures in long-term memory might have emerged during evolution as a way of providing structure to the elements being dealt with by working memory (Sweller, 2003, 2004). Thus, by allowing multiple elements to be treated as a single element in working memory, long-term memory schematic structures may have, as one of their functions, the reduction of working memory load. Specific schema selection in a particular situation is usually automated and quick. Our first impression about an unfamiliar person (which is said to be the most important), our comprehension of movies, fiction, music, humor, or art is guided by our acquired domain-specific schematic knowledge structures. Schemas guide our recall of different past events. Our memory usually retains the gist of a situation or event according to our schematic knowledge of it. The schema defines what is encoded and stored. When recalling the event, we create schema instantiations filling in missing information and inferring unavailable components using our schemas for the event. Sometimes such recall may produce various distortions to fit our schemas or expectations (e.g., recall scenes of court procedures from movies and fiction stories with witnesses remembering details they have not actually witnessed). The structure of the schematic knowledge can be empirically assessed, for example, by asking students to group problems into clusters on the basis of similarity; to categorize problems after hearing only part of the text; to provide answers to problems when content words have been replaced by nonsense words; to solve problems when material in the text is ambiguous; to contrast problems using a nominated principle; to recall problems that were presented earlier; to identify which information within problems is necessary and sufficient for solution; and to classify problems in terms of whether the text of each problem provides sufficient, missing or irrelevant information for solution (‘text editing’) (Low & Over, 1992). Previously acquired schematic knowledge structures are the most important factor that influences learning new material. A student’s understanding of an instruction means instantiation of appropriate familiar schemas that would allow her or him to assimilate new information with prior knowledge. A failure to

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comprehend instruction might be caused by the lack of any appropriate schemas in LTM, by the lack of sufficient cues in the situation to elicit a schema, or by the learner applying a different schema than that intended by the instruction. Students' preexisting schemas often resist change: everything that cannot be understood within the available schematic frameworks is ignored or learned by rote. It is important to build new knowledge on top of students existing schemas or help them to acquire an appropriate schematic framework by relating it to something already known. Useful instructional techniques could be analogies or diagrams, to establish links with existing knowledge, and advance-organizers to elicit or activate existing relevant schemas or provide new ones (concept maps, headings, summaries at the start of chapters, etc.). Similar to production systems, a schema-based approach to representing knowledge provides a general framework that can be instantiated by specific theories. In all schema-based models of cognitive architecture, schemas are matched to the contents of working memory for recognition. If a schema is partially matched by the information in working memory, it will create further information to complete the match. Schemas instantiated in working memory could be modified or reorganized, then placed back into long-term memory and serve as a new, more specific schema for further recognition. Schema theories do not differentiate between procedural and declarative knowledge. Instructions for actions may be produced by matching a schema to a situation and adding missing pieces of information. For example, recognizing a situation as a schema for solving simple linear algebraic equation and recognizing values of corresponding slots would provides directions for necessary operations. Production rules could be considered as a form of schematic knowledge. There is a tendency towards converging production system and schema-based approaches within those approaches. For example, Koedinger and Anderson (1990) integrated two approaches by constructing a computational (production-system-style) model of solving geometry problems using schema-based knowledge structures. The schemas (‘diagram configuration schemas’) were described as clusters of geometry facts that were associated with a single prototypical geometric image. In this book, schematic knowledge structures will be used as the basic unit and prevailing form of knowledge representations in long-term memory. Accordingly, the approach to human performance that is based on studies of schematic knowledge structures will be further referred to as a schema approach.

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PROBLEM SOLVING AND THE NATURE OF HUMAN EXPERTISE All of our purposeful cognitive activities can be considered as problem solving. Initially, in the 1950s and 1960s, most research studies on problem solving were concerned with knowledge-lean task domains that required no special training or background knowledge (for example, the famous ‘Tower of Hanoi’ task, various puzzles, etc.). The study of such tasks led to the formulation of a general theory of human problem solving (Newell & Simon, 1972). In this theory, a problem contains three main components: a given state, a goal state, and a set of operators for transforming the given state into the goal state. Problemsolving activity is considered as a search in the problem space that consists of separate problem states (knowledge states). The task of problem solving is to find a sequence of operators that can transform the initial state into a goal state within the problem space. So-called weak methods could be used in solving knowledge-lean tasks. We often use general heuristics (rules of thumb) for choosing necessary sequences of operators. For example, the difference reduction heuristic suggests choosing operators that maximally reduce the difference between the current state and the desired state. However, this method does not guarantee success in solving the problem, and more advanced methods are usually adopted. Forward chaining starts with the initial problem state, and a selected heuristics-based operator is applied, and then the strategy repeats. Backward chaining starts with the desired solution state, and a heuristically chosen operator is applied in reverse. A subgoaling strategy chooses an operator and forms a subgoal to find a way to change the current state so that the chosen operator could be applied. The method of solving by analogy uses the structure of the solution to one problem to obtain the solution to another problem (van Lehn, 1989). The weak methods are often used in combined forms. For example, the GPS (General Problem Solver) production system-based mechanism developed by Newell and Simon (1972) uses the means-ends analysis method. This method consists of looking for an operation that reduces the difference between the goal and initial state, setting up subgoals whose solution provides a solution of the original goal, and building up a hierarchical plan to solve a problem. Means-ends analysis thus combines forward chaining and operator subgoaling: the current state of problem solving is compared to the goal state and actions are selected to reduce the difference (van Lehn, 1989).

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In the early 1980s, experiments with puzzle problems demonstrated that, even after extensive problem solving by means-ends analysis, participants still did not induce a simple solution rule. Rule induction occurred only after some additional information had been provided (Mawer & Sweller, 1982; Sweller & Levine, 1982; Sweller, Mawer, & Howe, 1982). Empirical evidence was obtained that extensive practice in conventional problem solving was not an effective way of acquiring schemas that are required to successfully solve corresponding problems (Owen & Sweller, 1985; Sweller & Cooper, 1985; Sweller & Levine, 1982; Sweller, Mawer, & Ward, 1983). These studies suggested that a means-ends strategy could inhibit schema acquisition. A means-ends strategy focuses attention on specific features of the problem situation required to reach the goal and on reducing difference between current and goal problem states by selecting proper operators. Maintaining subgoals and considering alternative solution pathways are cognitively demanding mental activities that might result in working memory overload. Additionally, these activities are unrelated to learning solution schemas that are critical for successful future problem solving. They reduce resources devoted to learning other important aspects of problem structure. For example, studies of two-step problems demonstrated that cognitive load might be very high at the subgoal stages resulting in more errors than on the final goal stage (Ayres & Sweller, 1990). Sweller & Levine (1982) demonstrated rapid learning of maze problemsolving schemas when the specific goal state was unknown, and it was not possible to reduce differences between the goal and given problem states. Sweller, Mawer, and Ward (1983) found that using a means-ends strategy can actually impair learning, and that less directed exploration of the problems facilitated acquisition of useful problem schemas. They used simple physical and geometry problems without a specific goal stated (goal-free problems such as Calculate the value of as many variables as you can) and observed enhanced development of problem-solving skills. Owen and Sweller (1985) found that problem solvers using a means-ends strategy made significantly more errors than those using other methods, supposedly due to the working memory load associated with meansends analysis. In a theoretical investigation of the cognitive (working memory) load phenomena, Sweller (1988) constructed and analyzed a computational model of cognitive processes based on a theory of production systems (Newell & Simon, 1972). The model operates by matching elements on the condition side of each production to elements in a working memory (for example, the knowns, unknowns, goal, possible equations or theorems). If the condition side of a production is matched by some of the elements in working memory, the

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production can fire, and its action alters the content of working memory allowing other productions to fire. The cognitive load in such a model could be measured considering the number of statements in working memory, the number of productions, the number of cycles to solution, and the total number of conditions matched. Application of this model to novice cognitive behavior in various instructional procedures provided evidence of the heavy cognitive load associated with a means-ends strategy compared with a forward-working goal-free strategy. It also explained why the use of goal-free problems or worked examples was more effective means of acquiring schemas than conventional problem solving (Sweller, 1988; Ayres & Sweller, 1990). Since the late 1970s, the research focus in problem solving shifted to studying knowledge-rich task domains (algebra, geometry, physics, thermodynamics, computer programming, chess, bridge, etc.) that required an essential knowledge base as a prerequisite. Problem solving in such domains has additional complexities. Representation of a problem requires a great deal of domain knowledge, and operators that are usually used are domain-specific operators. The central questions of research in such domains are how is knowledge used to build up a problem representation and how does it influence the actual problem-solving process (Reimann & Chi, 1989). In semantically rich domains, problem solving involves searching one's knowledge of the domain in order to find the operators for solving the problem. Research on the use of knowledge in problem solving suggests that people use two types of domain-specific knowledge to solve problems: declarative conceptual knowledge (knowledge of the principles of the domain) and procedural knowledge (knowledge how to perform cognitive activities). Procedural knowledge may be described as a set of production rules that define actions for achieving goals (Anderson, 1983). Conceptual and procedural knowledge in problem solving can be considered as organized into problem schemas. They form the general framework of knowledge that corresponds to classes of problems. Problem solving in complex domains thus can be viewed as finding an appropriate problem schema in long-term memory and filling in this schema with the specific parameters of the problem (Chi, Feltovich, & Glaser 1981; Chi & Glaser, 1985). The problem schema determines what conceptual knowledge is used to build a representation of the problem statement, and what procedures are used to solve the problem. Much research in knowledge-rich domains is concerned with the differences between expert and novice problem solving. It has become evident that experts' behavior is mostly determined by their knowledge base. Therefore, the learning processes in which the experts acquired this knowledge are critical in explaining their performance. The focus of attention in

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the later studies shifted to learning theories as theories of the acquisition of expertise (Van Lehn, 1989). A considerable number of recent research studies in cognitive psychology have been concerned with the investigation of the structures and processes of human competent performance as a consequence of learning. It is generally accepted that development of expert performance is a very complex process involving a great deal of deliberate effort. Studies have shown that at least 10 years of practice are necessary for people in various fields of culture and science to reach superior levels of skilled performance (Ericsson & Charness, 1994; Ericsson, Krampe, & Tesch-Romer, 1993; Simon & Chase, 1973). Expert performance is usually acquired during extensive deliberate practice in a domain. Such practice should be organized at an appropriate and challenging level of difficulty, allow steady skill refinement by repetition and error correction, and provide informative feedback to the learner (Ericsson et al., 1993; Ericsson & Lehman, 1996). Competent expert performance generally requires well-developed cognitive skills, well-organized structures of knowledge, and self-regulatory performance control or metacognitive strategies (Glaser, 1990). Well developed cognitive skills as a major characteristic of expert performance require functional (related to conditions of applicability) automated knowledge (Fitts & Posner, 1967; Anderson, 1983, 1993; Klahr, Langley, & Neches, 1987). The process of skill learning is claimed to occur in several stages. In the first stage (cognitive stage), a description of the procedure is learned in the form of declarative knowledge. In the second stage (an associative stage), the declarative information is transformed into a procedural form, and a set of procedures for performing the skill is acquired. Such a process of converting declarative knowledge into a procedural form is called proceduralization. In this stage, two forms of knowledge (declarative and procedural) coexist. In the third stage (autonomous stage), the skill becomes more rapid and automatic (Anderson, 1983). When knowledge becomes automated during the development of proficiency, conscious processing capacity can be concentrated on higher levels of cognition. Automated performance requires a limited attentional capacity. Processing that once demanded active control, after extensive practice can become automatic, freeing limited attentional capacity for other tasks (Kotovsky, Hayes, & Simon, 1985; Schneider& Shiffrin, 1977; Shiffrin & Schneider, 1977). For example, while the use of declarative knowledge initially requires much conscious cognitive processing, automatic application of proceduralized knowledge frees working memory and allows its capacity to be used for the processing of new knowledge. Intensive training on certain procedural elements of a task can make

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them more automatic and free cognitive capacity for other more creative elements. This is especially important for transfer of training (Cooper & Sweller, 1987; Howell & Cooke, 1989). Automated lower level routine procedures enable learners to concentrate on finding new ways of applying their knowledge in unfamiliar situations. The process of learning could be considered as the acquisition of new schemas that eliminate the need to apply weak problem-solving methods (e.g., means-ends analysis) to solve future similar problems. The result is a shift from a novice strategy of working backward from the goal using means-ends analysis and subgoaling, to a more expert knowledge-based strategy of working forward from the initial state to the goal. Availability of a sufficient set of relevant domain-specific schematic knowledge structures that could be used in performing tasks is an important feature of a competent human performance. With experience in a domain, knowledge is organized into larger interconnected aggregate structures that explain the skilled performance of experts (Chi, Glaser, & Farr, 1988; Lord & Maher, 1991). Under a schema-based approach, learning can take different forms. Schema evolution is a central mechanism in the development of expertise. New information could be encoded in terms of existing schemas without involving any new schemas. Schemas evolve as they are applied and utilized as learner experience in the domain increases. Another form of learning is restructuring or creation of new schemas. In order to explain how schemas can be built up through experience, Rumelhart and Norman (1981) proposed a mechanism of learning by analogy. Initially, a new schema could be created by modeling it on an existing schema followed by a process of refinement (tuning). When a learner encounters a new situation in a familiar domain, she or he tries to interpret it using existing schemas. If none of them suits the situation, the best existing schema can serve as a model from which to start the tuning process. The characteristics of this model that do not contradict the new situation are carried over into the new schema. Planning and self-regulatory (metacognitive) skills allow experts to control their performance, assess their work, and predict its results. These self-regulatory skills are an important condition of expert ability to use the available knowledge base (Chi, Bassok, Lewis, Reimann, & Glaser, 1989; Larkin, McDermott, Simon, & Simon, 1980). Chi et al. (1989) proposed that students learn and understand examples of problem solutions via the self-explanations they give while studying. Students who are successful problem-solvers tend to study example exercises by explaining and providing justifications for each action and relating these actions to the principles and concepts of the domain. These students read the example with understanding and self-monitoring. Students who are less successful

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problem-solvers do not connect their explanations (if any) with their understanding of the principles of the domain. During problem solving, successful students may use examples for a specific reference, whereas less successful students repeat them in search for ready-made solutions. The level of performance significantly depends on the metacognitive skills that learners bring to the task. Cognitive studies of human performance and learning have the potential to greatly influence instructional design principles. Generally, instructional design should minimize learners' involvement in activities that overburden their limited working memory and be adapted to the learners’ available knowledge structures in long-term memory. Appropriate design of instruction should be based on the knowledge of characteristics of expert performance, expert-novice differences, and the transition process from novice to expert. Cognitive models of expert performance and their influence on the design of instruction are considered in the following chapter.

Chapter 2

COGNITIVE STUDIES OF EXPERT-NOVICE DIFFERENCES AND DESIGN OF INSTRUCTION SCHEMA-BASED APPROACH TO STUDYING EXPERT PERFORMANCE The purpose of cognitive studies of human expertise is to identify the cognitive structures and processes responsible for skilled performance. Expert performance has been studied in a variety of domains, for example, chess (de Groot, 1965), physics (Chi, Feltovich, & Glaser, 1981; Larkin, McDermott, Simon, & Simon, 1980), programming (Anderson, Boyle, & Reiser, 1985) and radiology (Lesgold, Rubinson, Feltovich, Glaser, Klopfer, & Wang, 1988), to name just a few. Various techniques and approaches have been applied to find out the organization of experts' knowledge, the characteristics of their understanding, information processing requirements and the nature of competency in such areas as chess (Chase & Simon, 1973; Simon, 1979), geometry (Greeno, 1977), genetics (Smith & Goodman, 1984), physics (Larkin & Reif, 1976), electronic troubleshooting (Brown & Duguid, 1989; Forbus & Gentner, 1986; Gitomer, 1988; Lesgold & Lajoie, 1991; Morris & Rouse, 1985; Perez, 1991; Rasmussen, 1986; Swezey, Perez, & Allen, 1988; Tenney & Kurland, 1988; Wiggs & Perez, 1988), and mechanical troubleshooting (de Kleer & Brown, 1983, 1984; diSesssa, 1983; Forbus, 1984; Hegarty, 1991; Hegarty & Just, 1989; Heller & Reif, 1984; Miyake, 1986; Reif, 1987; Stanfill, 1983; White, 1983; White & Frederiksen, 1986). As discussed in the previous chapter, schemas are a major type of knowledge representation in long-term memory that reflects prototypical features of objects,

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situations, and events. To understand or interpret incoming information, the human cognitive system matches this information with existing schemas (Rumelhart & Norman, 1983). In general, studies of expert-novice differences demonstrate that expertise is not so much a function of superior problem-solving strategies or a better working memory, but rather experts have a better domainspecific schematic knowledge base. Chunks have played an important role in the development of the understanding of expert-novice differences. Since Miller's (1956) finding that short-term memory is limited to approximately seven units, or chunks, of information, a chunk has served as a unit of measurement for memory capacity. A chunk can be considered as a generalized example of a schema. De Groot (1965; 1966) was one of the first psychologists who investigated expert-novice differences and demonstrated that expertise can be explained by the enormous amounts of knowledge that experts can access. In his classic studies, chess players had to reconstruct the positions of chess pieces on a board, after a brief exposure (5 seconds). De Groot's findings that chess masters could recall many more pieces from briefly exposed real chess positions than novices was explained by masters having larger chunks. Chase and Simon (1973) noticed that experts placed chess pieces on the board in groups that represented meaningful configurations. The experts did not show superior performance when random placements of the chess pieces were used. Egan and Schwartz (1979) studied expertise in electronics with a methodology similar to that used by Chase and Simon (1973) in studying chess expertise. They found that experts could reconstruct large circuit diagrams from memory recalling them in chunks of meaningfully related components. The experts were better than novices at recalling meaningful (not random) circuit diagrams. The size, rather than number, of recalled chunks increased with study time. Chase and Ericsson (1982) further suggested that the superior memory of chess masters and other experts was due to possession of schema structures with specific slots filled in with the index information that served as retrieval cues. The material could be recalled by reading out the contents of these slots and selecting schemas that corresponded to familiar stimuli. The schema-based approach was successfully used to explain various phenomena related to expert performance and differences between experts and novices (Chi et al., 1981; Reimann & Chi, 1989). For example, in the domain of physics, experts' categories were based on the principles of mechanics (conservation of energy and momentum, etc.), whereas novices' categories were based on objects and surface features stated in each specific problem (incline plane, spring, etc.). In the case of an object being balanced on an inclined plane,

Cognitive Studies of Expert-Novice Differences and Design of Instruction 23 the experts saw it as an example of a class of problems requiring a balance-offorces approach, while novices saw it as an inclined planes problem type. The failure of a novice to solve this problem may result from the fact that different incline plane tasks may require different approaches (based on balance of forces, energy conservation, etc.), and the presence of the incline plane alone does not determine the appropriate approach. One of the reasons for novices' difficulties in problem solving is that they activate only lower-level schemas that incorporate only surface aspects of the problem, whereas experts activate higher-level schemas that contain information critical to the problem solution (Chi & Glaser, 1985). Thus, experts categorize problems in terms of deep structures such as the laws used to solve the problems, while novices categorize problems based on surface structures such as common physical attributes. The same problem may elicit different schemas for experts than for novices. Schematic knowledge structures in long-term memory effectively provide necessary executive guidance during high-level cognitive processing (Sweller, 2003). Without such guidance and in the absence of external instructions, people usually resort to random search or weak problem-solving methods such as meansends analysis (a gradual reduction of differences between current and goal problem states). Such methods are cognitively inefficient and time consuming. They may impose a heavy working memory load interfering with construction of new schemas (Sweller, 1988). In contrast, when experts in a domain encounter a familiar problem situation, they rapidly retrieve appropriate previously acquired schemas from long-term memory and apply them in a cognitively efficient way (Chi, et al., 1981; Larkin, et al., 1980). Schemas allow them to categorize different problem states and decide the most appropriate solutions. Due to their available knowledge base in long-term memory, experts are able to avoid cognitively inefficient mental activities and perform with greater accuracy and lower cognitive loads. Schematic knowledge structures can be described functionally by indicating how a person with a specific level of a schema acquisition would act in relevant problem situations. For example, without any schematic knowledge of procedures for solving the equation 4x + 2 = 3 and in absence of any guidance, a student will treat each symbol separately and may try to use a means-ends analysis approach by reducing differences between a current problem state and the goal state (x = ?) or attempt to apply various random operations to the numbers. With some previously acquired knowledge of an appropriate procedure, another student may immediately proceed to subtract the coefficient 2 from both sides of the equation: 4x + 2 – 2 = 3 – 2. The whole combination of elements (e.g.

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4x + 2) will be treated as a meaningful single unit or chunk. If a student practiced considerably with this kind of equations, the schema for this procedure may be automated and her or his first solution step will be 4x = 1. Another, even more experienced student may have all the relevant solution procedures well learned or automated and would write the final answer (x = 1/4) almost immediately. Similar examples of expert-novice differences could be demonstrated in other areas. Each symbol in a wiring diagram could be treated as a separate element by a novice electrician, while an experienced professional would see the whole diagram as representing a complete system. For a foreign language non-speaker, a printed text might look as a collection of unfamiliar symbols, while fluent native readers would be able to make sense out of the whole text. They would treat words or even combinations of words as single elements. By combining multiple elements of information into a single chunk in working memory, long-term memory schemas allow experts to avoid processing overwhelming amounts of information and to effectively reduce working memory load during high-level cognitive processing. In addition, experts are also able to bypass working memory limitations by having many of their schemas highly automated due to extensive practice. Human cognitive architecture has evolved in a way that information processing changes significantly as this information becomes more familiar to an individual (Sweller, 2003). Schematic knowledge structures held in long-term memory significantly influence the content and characteristics of working memory by effectively transforming it into long-term working memory (Ericsson & Kintsch, 1995). An expert’s routine problem solving in a familiar domain usually involves a selection of an appropriate schema, adapting it to the problem, and executing the solution procedure. Often it occurs as a direct recognition early in the perception of the problem (Chi, Feltovich, & Glaser, 1981). Non-routine problem solving includes additional procedures such as search (when more than one schema is applicable to the situation) or combining the schemas (when no one schema will cover the whole problem) (Larkin, 1985). Substantial evidence has accumulated that a schema theory of problem solving can be successfully used to explain experts' performance in various task domains (Reimann & Chi, 1989). Building a problem representation is a key process in problem solving (Larkin, 1985; McDermott & Larkin, 1978, Simon & Simon, 1978). It has been found that experts spend more time on a qualitative analysis of the problem and building explicit representations of the situation (for example, by drawing the diagrams of causal relationships between the objects). Experts also form more abstract and enriched representations than novices do. For example, according to Chi, Feltovich, and Glaser (1981), experts classify physics problems based on

Cognitive Studies of Expert-Novice Differences and Design of Instruction 25 abstract physics categories and principles, while novices do it according to surface characteristics of the problem. Thus, the level of problem representation depends on the solver's problem schemas. An initial cue (first sentences in the problem statement, etc.) may activate a particular schema that is then matched to the problem. Any mismatch results in the rejection of that schema and triggering of another schema. Successful problem solving in technical domains depends on the solver's schemas for the causal relations between components of a technical system which allow mental simulations of the system operation (de Kleer & Brown, 1983; Gentner & Stevens, 1983; Miyake, 1986). Providing learners with a causal description of a device’s operation in addition to information about its components was shown to enhance their ability to operate the device (Kieras & Bovair, 1984; Mayer, 1989a). Different types of schemas are appropriate for solving different types of problems. At higher levels of skill, the choice of schematic knowledge types is determined by higher level structures in which an expert's representations are organized (Hegarty, 1991). Initially, problem schemas are specific to the situations from which they were induced. With experience, they become indexed by the general principles and problem solving becomes faster and takes less effort. Organization of the solvers' knowledge into large groups of chunks or schemas decreases the demands on working memory and allows learners to activate appropriate procedures. As soon as experts retrieve a problem schema, they automatically access the procedures for solving the problem (Chi et al., 1981; Smith, 1991). The development of a problem representation can be viewed as the sequential attempts of schema refining, which depends on the structure of the domainspecific knowledge of the solver. This results in experts spending more time on planning and using forward-working and efficient problem-solving processes (Reimann & Chi, 1989). Empirical studies in various domains have revealed that problem-solving strategies are determined by the nature of the problem representations, differences in the organization of knowledge, and the number of domain-specific problem schemas that solvers have because of their experience in a domain (Larkin, 1985; Lesgold, Feltovich, Glaser, & Wang, 1981). Experts’ performance is schema-driven. Experts possess more domainspecific schemas and can access and use them more efficiently than novices. Experts work forward deriving the appropriate problem schema from the problem statement. In contrast, novices’ performance is goal-driven. Novices work backward from the goal, searching for operators that will allow them to derive the needed solution. However, working backwards is a default strategy that both

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experts and novices use when there is no schema for a given type of problems. In a novel situation, experts use various types of general heuristics together with domain-specific knowledge (Perkins, Schwartz & Simmon, 1991; Rist, 1989; Schultz & Luchheud, 1991). Thus, expert performance depends on available problem representations, knowledge base (facts, concepts, principles, knowledge of a system and rules how to use this knowledge), availability of appropriate domain-specific schemas, general procedures (strategies, heuristics, algorithms), and relations among all these elements (Hart, 1986; Lesgold and Lajoie, 1991). According to Chi, Glaser, and Farr (1988), the main features of competent expert performance are: 1) domain-specificity (experts exhibit superior performance mainly in their own domains); 2) perception of problem situations by large meaningful patterns; 3) high speed of performance; 4) superior well-organized long-term memory knowledge base; 5) deep-level and principle-based problem representations; 6) thorough qualitative analysis of problems; and 7) strong self-monitoring skills.

COGNITIVE STUDIES OF EXPERT-NOVICE DIFFERENCES AND INSTRUCTIONAL APPROACHES Most studies of expertise have focused on discrete expert-novice differences in solving specific tasks. Existence of a continuum between novices and experts has been frequently ignored. As a result, our knowledge about the development of expertise and about changes in cognitive processes as expertise is acquired is limited. Groen and Patel (1991) suggested four developmental levels: 1) novices with no training in the domain (possessing only common sense knowledge and everyday experience); 2) intermediates who have received some instruction in the domain; 3) sub-experts who have expertise in a closely related domain (they may also be viewed as intermediates); and 4) experts who are always correct in solving routine problems and solve them by way of forward reasoning. It is impossible for novices to learn expert approaches directly. When expert rules are taught to beginners, they form isolated pieces of knowledge that are not retained for a long period of time (Groen & Patel, 1991). Thus, an existing theory of expert performance cannot be applied directly to instruction, and theoretical models of student transition from one level to another should be developed.

Cognitive Studies of Expert-Novice Differences and Design of Instruction 27 Expert routine problem solving is traditionally associated with using a forward-working strategy; novices tend to work backward. In the case of unfamiliar problems experts also use backward reasoning. The studies of Sweller and his colleagues (Mawer & Sweller, 1982; Sweller & Levine, 1982; Sweller et al., 1983) brought some understanding of when the switch occurs during the development of expertise and what factors would facilitate the switch. It was demonstrated that means-ends analysis might prevent the acquisition of problemspecific rules because this method could leave no cognitive resources available for meaningful learning. Rule acquisition occurred or improved under conditions where subjects were provided with information additional to the problem goal (for example, a set of subgoals) or were given goal-free problems. Sweller et al., (1983) hypothesized that the main factor responsible for this result was the kind of information a learner focuses on during problem solving. If knowledge or schema acquisition is an aim of problem solving, then the influence of the goal as a control mechanism should be reduced. In some studies, forward reasoning intermediate level medical students performed more poorly then either experts or novices (Groen & Patel, 1991). This result was explained by their dogmatic reliance on existing basic science knowledge. When students' knowledge contains misconceptions, forward reasoning might be harmful for learning. If they reasoned backward, then the misconceptions would be just temporary hypotheses. It was suggested that in such cases an emphasis should be placed on self-explanations and testing their adequacy (explanation-based learning) rather than on correct problem solving (Groen & Patel, 1991). Most of the experimental evidence in the area of expert-novice differences was obtained by contrasting performance of experts and novices. Schoenfeld and Hermann (1982) conducted one of the first longitudinal studies of the relationship between problem perception and expertise. Students' perceptions of mathematical problems were examined before and after intensive training in mathematical problem solving. It was demonstrated that novices sorted problems based on surface components mentioned in the problem statement. After the training, they sorted them in a more expert-like way according to the principles of problem solution. Thus, problem perception and problem schemas on which such perception is based changed as learners became more experienced in the domain. With the development of expertise, problem schemas change in their level of specificity (diSessa, 1983; Forbus & Gentner, 1986; Kaiser, Jonides, & Alexander, 1986). Initially induced from specific situations, they become more general and indexed by the underlying principles (Chi et al., 1981). At higher

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levels of development, schemas may also change from qualitative to quantitative representing relationships between components of problem situations more precisely (Forbus & Gentner, 1986; Hegarty, Just, & Morrison, 1988). As people gain more experience with technical systems, they learn relations between their common subsystems and learn to chunk components of systems into these subsystems (Hegarty, 1991). New information is then assimilated into existing sophisticated knowledge structures. The learning mechanisms and strategies evolve as a learner becomes more experienced (Langley & Simon, 1981). Lesgold et al. (1988) hypothesized that early learning is perceptual and different from later cognitive learning. Experts use schemas to interpret incoming information, intermediates often reshape their perceptions to fit the schema, whereas novices completely rely on their perceptions. The previously mentioned decline in performance at intermediate levels can also be due to the shift from perceptual learning to cognitive schemabased learning. According to the triarchic/global/local architecture of expert cognition (Sternberg & Frensch, 1992), when processing information from new domains, an expert relies mostly on controlled, global processing. If information belongs to the expert's narrow area of expertise, she or he relies mostly on automatic, local processing. Such local processing systems can operate in parallel, be automated, and characterized by almost unlimited processing capacity. As expertise develops, learned portions of processing procedures are transferred to a local processing system. This enables experts to automate more processing and thus to free global processing resources for dealing with new situations (Sternberg & Frensch, 1992). However, experts may be inflexible in new situations because it is difficult to reorganize an automated schema. Experiments with bridge players confirmed that experts were more affected when new task demands required changing deep, abstract principles rather than surface features. Novices were more affected by surface changes than by deep, abstract changes (Sternberg & Frensch, 1992). Nevertheless, Schraagen (1993) demonstrated that when domain-specific knowledge is missing, experts could still maintain a more structured approach than novices could by making use of more abstract high-level knowledge. According to the theory of skill acquisition (Anderson, 1983), the instruction in specific performance procedures must be preceded by the instruction in the concepts, rules, and principles of how things work (declarative knowledge). In addition to the theoretical principles, the ability to apply them in concrete situations should be developed (Morris & Rouse, 1985). A procedural approach only is not sufficient, because it is impossible to predict all possible situations in advance, especially in complex domains like modern digital electronics. Thus,

Cognitive Studies of Expert-Novice Differences and Design of Instruction 29 training should combine knowledge of system principles with procedures of how to use this knowledge in a specific context. In general, teaching expert performance might require a basic conceptual explanation of how things work, practice in carrying out basic procedures, and variation in experiences for tuning of procedural knowledge and the development of persistence and confidence (Gentner & Stevens, 1983; Greeno & Simon, 1988). Kieras and Bovair (1984) demonstrated that providing students with conceptual models of a complex system prior to information on how to use that system produced better recall, faster learning, and fewer errors in the operation of the system. Combined structural and functional descriptions of system operations are recommended for effective learning (Psotka, Massey, & Mutter, 1988). However, specific instructional strategies should be based on the cognitive requirements of particular tasks. The user does not always need a complete knowledge of the system in order to be able to operate it. For example, many experts in technical areas have a very limited understanding of general physics principles but satisfactorily perform their duties. If a device is simple, or a procedure is easily learned and practiced (e.g., a telephone) there may be no need to provide a device model. The user may infer a usable model without instruction (Kieras & Bovair, 1984). Limited underlying knowledge and understanding of how certain functions are fulfilled are required for operating and troubleshooting systems with simple functions. For more complex systems, a deeper understanding of their components and operation is required (Lesgold & Lajoie, 1991). Novices often have difficulties integrating general theoretical concepts with their intuitions because of conflicts between everyday meanings of new concepts (e.g., acceleration, mass) and their meaning in theory (Reif, 1987), conflicts between students' intuitive knowledge and theoretical laws (diSessa, 1982), or because of the lack of procedural knowledge of solving specific problems that is often not explicitly taught (Heller & Reif, 1984). There have been two major approaches in using the results of cognitive research on knowledge structures in the design of instructional systems (Glaser, 1990). The first approach has been developed in the tradition of knowledge engineering in artificial intelligence and design of expert systems. It requires exposing the learner to the knowledge characteristics of well- developed expertise. The well-known example of a computer-based instructional system designed in accordance with this approach is the GUIDON project (Clancey & Letsinger, 1984). The second approach has been developed in cognitive science and is based on cognitive models of students' knowledge. For example, in instructional systems

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based on qualitative models (Chi, 1988; Forbus & Gentner, 1986), a learner has to progress from simple to more sophisticated domain-specific conceptual models (e.g., coordinated functional, causal, and structural models; qualitative and quantitative models). This progression occurs in the context of solving specifically designed problems with gradually increasing levels of complexity. An example of this approach is the program for teaching troubleshooting of electric circuits QUEST (White & Frederiksen, 1986). Similar ideas were realized in the STEAMER project (the simulator for training engineers to operate steam propulsion plants aboard large naval ships). The primary goal was to teach a robust conceptual model (rather than specific procedures) that could be used to reason about the steam plant qualitatively (Holland, Hutchins, McCandless, Rosenstein, & Weitzman, 1987). Abstract graphic images of the steam plant were organized in a hierarchical manner with the major plant parameters presented first, followed by more detailed simulations of subsystem components. SHERLOCK is an example of a coached-practice learning environment in which learners compare their own performance with expert performance (Gabrys, Weiner, & Lesgold, 1993; Lesgold and Lajoie, 1991). Such reflection, however, may place a large demand on working memory, if solution paths are long or complicated. SHERLOCK supports reflection by a replay of the trainee's and an expert's performance. During replay, the system provides a summary of the information the user has obtained on previous steps. The system allows learners to observe the expert's decision process, reasons behind it, and the overall goal structure for the expert performance. This technique reduces the cognitive load associated with remembering the details of trainee's own performance while observing the expert's actions (Gabrys et al., 1993). Another well-known example of a similar approach is the model-tracing methodology in intelligent tutoring systems (Anderson, 1993). The tutoring system simulates a student’s cognitive behavior in real time and maintains a model of the student's knowledge state. It provides an example-based learning environment in which students can induce rules from examples. The learner's actual performance is compared to the ideal structure of solution (production rules model), and the student is kept on the correct solution path. The tutor estimates the availability of acquired productions based on their correct and incorrect applications and selects appropriate problems for exercises. Many tutoring programs based on the model-tracing methodology have been effectively used in the fields of programming, geometry proofs, solving algebraic equations (Anderson, Boyle, & Reiser, 1985; Anderson & Corbett, 1993; Anderson,

Cognitive Studies of Expert-Novice Differences and Design of Instruction 31 Corbett, Fincham, Hoffman, & Pelletier, 1992; Anderson, Farrell, & Sauers, 1984).

COGNITIVE MODELS OF DEVELOPMENT OF EXPERTISE AND INSTRUCTIONAL DESIGN Cognitive studies of human performance and learning have demonstrated that learning processes are supported by a basic cognitive architecture that includes a powerful long-term memory and a limited working memory. Schema acquisition and automation as the major learning mechanisms are critical in intellectual skills formation. Studies of chess skills and other domains indicate that our knowledge base provides the foundation of intellectual skills. Schemas held in long-term memory allow experts to avoid processing overwhelming amounts of information in working memory and thus by-pass working memory limitations. Automatic processing allows mental processes to occur rapidly, smoothly, without conscious control and associated burden on working memory. With time and practice, all cognitive processes can occur automatically (van Merriënboer & Paas, 1990). For example, initially solving a/b=c for a needs considering the problem consciously before realizing that it belongs to the category that requires multiplying out the denominator. After substantial practice, the schema becomes automated and allows instant recognition of the category of this problem (Sweller & Chandler, 1994). Initially, a novice learner deals with isolated pieces of information without an organizing structure. Available lower-level schemas could be used to interpret these isolated pieces of information. After studying relevant examples and problem-solving practice, isolated pieces of information may form higher order structures according to similarities and relationships among them. As a result, new schemas are formed. In the next phase, new facts are added, schemas become more integrated (schemas consisting of other schemas rather than facts), and performance becomes more automated and unconscious. Such a transition between different phases of learning is a continuous process and boundaries between phases are vague (Shuell, 1990). Learners acquire new meaningful knowledge by integrating their existing knowledge structures with schemas induced from studying examples or problem solving, for example, by explaining each step to themselves in terms of knowledge they had already acquired (Chi et al., 1989). A learner may not understand an instruction if she or he lacks an appropriate schema or cues to retrieve it, or if the learner activates a different schema from that intended by the

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instruction (Rumelhart, 1980). Students' schemas might interfere with instruction when there are mismatches between the existing schemas and those the instructional designers assumed they had (Osborne & Schollum, 1983). Thus, in order to be understood, instruction should correspond to the students' existing schemas. However, students' own schemas in particular domains are often quite different from those of experts or teachers. These schemas (alternative frameworks, misconceptions, preconceptions, phenomenological primitives) might make much information incomprehensible and require special procedures to alter them (diSessa, 1993; Howard, 1987; Slotta, Chi, & Juram, 1995). Some means to determine students' misconceptions are interviews, analysis of students' reasoning in problem solving, word associations (associations to a concept name as indicators of the underlying conceptual structure), and concept mapping to represent key concepts and relationships (Howard, 1987; Sutton, 1980). General principles derived from cognitive research suggest that in order to provide consistency between instruction and cognitive processes leading to expert performance, instruction should be adapted to a learner's prior knowledge, and the learner should be actively involved in development of the skills (Tannenbaum & Yukl, 1992). Cognitive task analysis could be used to determine underlying knowledge structures and cognitive skills required for the task. For example, Gagne (1984) suggested identifying for each part of a task what a person must be able to do in order to perform it. Reigeluth (1983) proposed a general-to-specific approach which requires identifying the main idea followed by determining the specific aspects of this idea. Broader concepts are consequently differentiated into ones that are more specific. Knowledge engineering methods that have been developed in the field of artificial intelligence could also be used to extract expert knowledge structures and use them in the design of instructional materials. In the framework of a competency-based approach, van Merriënboer (1997) developed the four-component instructional design model (4C/ID). This model provides methods for analysis of complex cognitive skills, knowledge structures required for performing the skills, and development of appropriate sequences of whole task practice situations that would support acquisition of those skills. The model takes into account the limited working memory processing capacity by gradually increasing the level of cognitive load imposed by the sequences of whole tasks (van Merriënboer, Kirschner, & Kester, 2003). A set of software tools to assist designers in applying the 4C/ID methodology has been also developed (de Croock, Paas, Schlanbusch, & van Merriënboer, 2002). According to the 4C/ID methodology, cognitively complex learning environments include four interconnected components: 1) learning tasks organized in a simple-to-complex sequence of task classes with gradually

Cognitive Studies of Expert-Novice Differences and Design of Instruction 33 diminishing levels of support within each class (process of scaffolding); 2) supportive information for more general aspects of the learning tasks that change over different specific problem situations; 3) just-in-time (algorithmic) information for invariant aspects of the learning tasks; and 4) part-task practice providing additional repetitive practice for constituent skills that need to be performed at a very high level of automaticity (van Merriënboer, Clark, & de Croock, 2002). Development of these four components requires deconstructing complex skills to build an intertwined skills hierarchy; sequencing task classes around authentic problem situations; analyzing mental models and cognitive strategies to describe knowledge structures guiding non-recurrent aspects of competent performance; analyzing rules and procedures, and prerequisite knowledge supporting recurrent skills; and selecting appropriate timing of supportive and procedural information presentation (Kester, Kirschner, & van Merriënboer, 2004; van Merriënboer & Dijkstra, 1997; van Merriënboer, Jelsma, & Paas, 1992). A cognitive approach clearly distinguishes between the actual expert performance sequence and the instruction sequence (the sequence of learners’ activities designed to achieve desired instructional goals). Different instructional approaches could be used in designing instructions for separate parts of performance. For example, some skills might be developed initially to a high degree of efficiency to free working memory for the following changes in knowledge structures. In other cases, structures of conceptual knowledge could be taught at the beginning followed by practice with complex procedures (Glaser, 1990). Hierarchical, multi-level instructional sequences with explicit connections between different levels of the hierarchy could be more appropriate for building schematic knowledge structures than linear sequences (Reigeluth, 1983; Ausubel, Novack, & Hanesian, 1978). The big picture (or central idea, overview of the content) has to be presented first, followed by the specific knowledge. Moving from a central idea to its elaboration and back (zooming in and out) results in the acquisition of specific knowledge as part of whole rather than isolated information. Building hierarchical schematic knowledge structures in memory enhances retention and provides cognitive mapping of the material (Eylon & Reif, 1984). For example, advance organizers assist learners in understanding the organization of the content and relating it to already available knowledge. They usually include a brief general introduction to the following material at a higher level of abstraction using titles and graphical devices to highlight the hierarchical structure (Ausubel et al., 1978; Mayer, 1983; Mayer & Bromage, 1980). Mayer

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(1989a) demonstrated significant advantages of using conceptual models that highlight the major parts, states, and actions in the system as well as the causal relations among them. Such models helped learners to build internal models of the system by directing attention toward the important conceptual information, organizing the information and integrating it with existing knowledge. Similar conclusions were derived from the theories of analogical transfer that have pointed to the crucial role of learners' conceptual models in enabling transfer by mentally running these models in various situations (de Kleer & Brown, 1981, 1983; Gentner & Stevens, 1983). However, external conceptual models should be used cautiously when dealing with more advanced students who may possess well-organized schemas in the domain. The simplified conceptual models may conflict with the students’ more sophisticated knowledge structures and inhibit learning (Mayer, 1989a). White and Frederiksen (1986) suggested that these conflicts could be overcome by an appropriate instructional design that is based not only on the expert knowledge structures, but also on the knowledge of expert-novice differences and transition processes from novice to expert states. Cognitive conflicts between instructionbased conceptual models and learners’ internal knowledge structures may increase processing demands on their limited working memory. Cognitive load factors involved in complex cognitive performances and in the process of acquisition of expertise will be considered in the following chapter.

Chapter 3

COGNITIVE LOAD PERSPECTIVE IN INSTRUCTIONAL DESIGN THEORETICAL AND EMPIRICAL BACKGROUND OF COGNITIVE LOAD THEORY The concept of mental load was initially introduced in the 1950s and was based on the concept of a communication channel with limited capacity. Overloading this channel means operating above the limits of one's capacity resulting in errors or missed signals. An underload is associated with considerable spare capacity. Capacity theory of human information processing in relation to attention was originally developed to explain an operator’s limited ability to perform multiple activities simultaneously. Not specifying the nature of capacity or resources, it provided an explanation for performance decrements that occurred when the resource demands of the task exceeded the available supply (Kahneman, 1973; Navon, 1984). Specifying the nature of capacity or resources requires adopting a specific mechanism of human information processing or cognitive architecture. In the framework of the standard basic model of cognitive architecture, the working memory is associated with capacity limitations and cognitive resources consumption. Studies of cognitive load phenomena during problem solving clearly demonstrated that when cognitive load was greater than working memory capacity, learning was difficult, and schema acquisition and rule automation were inhibited. It was suggested that many traditional instructional materials were ineffective because they ignored limitations of the human cognitive processing system, especially the limited processing capacity of working memory. Cognitive

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load theory (Sweller, 1988; 1989; 1993; 1994; 1999; 2003; 2004; Sweller & Chandler, 1994; Sweller, van Merriënboer, & Paas, 1998; Chandler & Sweller, 1991, 1996; Paas & van Merriënboer, 1994b; Paas, Renkl, & Sweller, 2003, 2004) determined some important cognitive principles relevant to processing instructional information and their consequences for instructional design. Two independent sources of cognitive load that place demands on working memory capacity were initially proposed within the cognitive load theory. Intrinsic cognitive load is determined by the intellectual complexity of the instructional material to be learned. For example, operation of an intricate electrical circuit might be much more difficult to learn than working of any individual element of this circuit. Extraneous cognitive load is imposed solely by the format of instruction that can take various forms (written instructions, practical demonstrations, etc.) and require different activities of learners (solving problems, studying worked examples, etc.). Germane load, which is caused by cognitive activities that contribute to learning, has been introduced into the theory at a later stage to account for the learning-relevant demands on working memory (Paas & van Merriënboer, 1994a; Sweller, van Merriënboer, & Paas, 1998). For example, cognitive load caused by self-explanations during learning from worked examples represents an example of germane load. Such activities would obviously increase cognitive load, but would directly contribute to schema construction. The intrinsic, germane, and extraneous cognitive load combined result in the total cognitive load imposed on a learner. While intrinsic cognitive load is initially fixed for the learner, extraneous and germane cognitive load can be manipulated by instructional design. Cognitive load theory asserts that intrinsic load is determined by the degree of interactivity between individual learning elements. Any instructional material consists of elements of information that should be processed by learners. An element can be regarded as a learning item in its simplest form for a particular learner (Chandler & Sweller, 1996). If the elements can be processed individually, the information is considered low in element interactivity. It places little load on working memory because each element can be learned independently. For example, for a person learning individual words of a second language, intrinsic cognitive load is low because little or no interaction exists between learning elements. The task still might be difficult because there are many new words to learn. When learning elements need to be processed simultaneously, the material is high in element interactivity. Learning the syntax of a language (the appropriate order of words in a sentence) requires all the relevant words to be held in working memory simultaneously. Because all words interact, a sentence can only be

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understood if individual words and their relations are processed concurrently. This cognitive processing may increase the burden on working memory (Chandler & Sweller, 1996). The influence of element interactivity on cognitive load was demonstrated by Maybery, Bain, and Halford (1986) and Halford, Maybery, and Bain (1986). Using transitive interference problems (e.g., a is larger than b; b is larger than c; which is the largest?), they provided evidence that cognitive load was heaviest when learners attempted to integrate two premises. The integration required considering all the elements (a, b, and c) and their relations concurrently bringing element interactivity to its highest point. Thus, the intrinsic cognitive load caused by instructional material depends on the level of interaction between the elements of that material. Material including many elements, but with a low level of interactivity results in a low cognitive load. Difficulty in learning such material might not be due to working memory overload, but simply to the total number of elements to be learned. On the other hand, instructional materials that consist of heavily interacting elements might impose a significant cognitive load even if the number of elements is relatively small. Even simple electrical circuits with small number of components are usually difficult to learn because the elements of the circuits are highly interactive and must be learned as a whole and simultaneously (Sweller & Chandler, 1994). Assume a person is learning a simple application of a wiring configuration (the starter) for switching on/off a light by a single push on start/stop push buttons (Figure 3). While in isolation, all five elements used in the circuit (two push buttons, a switch, a light, and a coil) might be well known and simple. Combined in the circuit, they become interconnected and need to be considered simultaneously to understand the operation of the circuit. For example, to find out the state of the light (on or off) the learner should determine whether (1) the stopbutton is in its normally closed (not pushed on) position and (2) the switch is closed. The state of the switch depends on whether (3) the coil is energized. The state of the coil depends on whether (4) the start-button has been pushed on to energize the coil initially and (5) the stop-button has not been pushed on to interrupt the flow of current through the circuit. Thus, the state of the light depends on the states of all other components of the circuit. The number of elements and their relationships that must be considered simultaneously in this case is five. It must be emphasized that this estimation is based on the assumption that the learner is familiar with the operation of separate components of the circuit. For example, she or he understands that energizing the coil would close the switch, or that a single push on the stop-button would open the circuit. Otherwise, the number of elements to consider would expand considerably (Figure 4).

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Stop

Start

A

N coil switch

light

Figure 3. Electrical circuit for switching on/off a light by start/stop push buttons. Starter

Stop

Start

Normally closed push button switch 1

Normally open push button

Normally open switch

Figure 4. Components of the Starter.

The degree of element interactivity is not only determined by the nature of the instructional material. It is also influenced by the learner expertise in a particular instructional domain and her or his pre-acquired schemas in this area. Because schemas have a hierarchical structure, what is an element on one level may be a complex structure when a lower-order schema level is considered. With the development of expertise in a domain, lower-order schemas may become the elements of a higher-order schema. In other words, with expertise the size of a

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person's chunks (and the amount of information encapsulated within these chunks) increases. For example, a child learning to read must first acquire a schema for each letter of the alphabet to be able to recognize this letter in a variety of situations. These lower-order letter schemas later become the elements within higher-order word schemas. For experienced readers, these schemas act as elements in phrases and sentences (Sweller & Chandler, 1994). Higher-order schema acquisition reduces cognitive load by reducing the number of interacting elements in working memory. Many interacting elements for a novice may be a single element for an expert. For the learner in the above wiring circuit example, individual components of the circuit (push buttons, switch, light, and coil) acted as pre-acquired schemas. The learner was considered as an expert on the level of individual components. Once the interactions of the components of the circuit (the Starter) have been learned and the learner has become an expert in this class of electrical circuits, these lower-order schemas become the elements of a higher-order schema (the schema for a starter). This new schema can further act as a single element, and the above described element interactivity is no longer relevant. If this advanced learner encounters a Starter configuration of electrical components in a new wiring diagram, cognitive processing associated with these components probably will be carried out with minimal cognitive effort. These components will be considered as a single functional element in more complex electrical circuits, with the function of turning the circuits on or off. The noviceexpert distinction is always relative to the instructional goals: experts possess knowledge at the highest level targeted by the given set of instructional materials, whereas novices may possess only some lower-level knowledge. Thus, the intrinsic cognitive load imposed by the content of instructional materials is determined by its subjective degree of element interactivity that in turn depends on the learner’s level of expertise. On the other hand, extraneous cognitive load is associated with cognitive activity that a person is involved in because of the way the task is organized and presented, rather than because the load is essential for achieving instructional goals (Sweller, Chandler, Tierney, & Cooper, 1990; Sweller & Chandler, 1994). For example, when some interrelated elements of instruction (textual, graphical, audio, etc.) are separated over distance or time, their integration might require intense search processes and remembering some elements until other elements are attended and processed. Such processes require additional resources and might significantly increase total cognitive load. Consider the above simple electrical circuit (Figure 3) accompanied by the following separate textual explanations:

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Slava Kalyuga 1. The Starter consists of a start push button, a stop push button, and a switch activated by the coil. 2. Pressing down the start push button closes the circuit and allows the current to flow through the coil. 3. The energized coil closes the switch, which provides an alternative closed circuit for the coil to that provided by the start push button. The start push button now can be released without breaking the current flow through the coil. 4. The light is operational, as the closed switch provides a closed circuit for it. 5. To cease operation of the light the stop push button is pressed. The circuit in the Starter is now open, the coil is no longer energized, and the switch returns to its normal open position.

For a novice electrical trainee, understanding these instructions requires integration of the text and diagram. This may involve holding segments of text in working memory until corresponding components of the circuit's diagram are located, attended to, and processed; or keeping some images of the diagram active until corresponding fragments of the text are found, read, and processed. This search and match processes is likely to significantly increase extraneous cognitive load. Similarly, problem solving using means-ends analysis (see Chapter 1) usually involves a large number of interacting statements in working memory (e.g., interconnected subgoals and steps to solution). Such problem solving might require significant cognitive resources that become unavailable for learning. If learning is the goal of activity, then this cognitive demand should be considered as an extraneous cognitive load. According to cognitive load theory, when instructional material is characterized by a low intrinsic cognitive load, the extraneous cognitive load due to instructional design may be of little concern because total cognitive load may not exceed working memory capacity. In contrast, a heavy intrinsic cognitive load may be produced for many learners when instructional material is characterized by a high degree of element interactivity. In such a situation, an additional extraneous cognitive load caused by an inappropriate design can be harmful to learning because total cognitive load may exceed a learner's working memory capacity. There may be no cognitive resources available for meaningful learning, and schema acquisition and automation could be inhibited. In order for learning to be more effective, total cognitive load should be reduced. Thus, when instructional material has a high intrinsic load (i.e., high element interactivity) the extraneous cognitive load imposed by the instructional design

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may be critical for learning (Chandler & Sweller, 1996; Sweller & Chandler, 1994). Studies in cognitive load theory and its instructional implications have demonstrated that designing instructional materials in a way that reduces extraneous cognitive load can significantly improve learning. A brief review of those studies is presented in the following section. More extended reviews could be found in Mayer (2005) and Sweller (1999). In cognitive load research, working memory load has been measured through various methods, including computational models (Sweller, 1988), instructional processing times (Sweller, Chandler, Tierney, & Cooper, 1990), and dual-task paradigms (Brünken, Plass, & Leutner, 2003, 2004; Chandler & Sweller, 1996). For a comprehensive overview of cognitive load measurement methods, see Paas, Tuovinen, Tabbers, & van Gerven (2003). The dual-task paradigms use performance on a secondary task as an indicator of cognitive load associated with learning on a primary task. Various simple responses can be used as secondary tasks, for example, reaction times to some events such as a computer mouse click (Britton, Glynn, Meyer, & Penland, 1982; Lansman & Hunt, 1982), or counting backwards (Lindberg & Garling, 1982). For example, the secondary task used by Chandler and Sweller (1996) consisted of recalling the previous letter seen on the screen of a separate computer while encoding the new letter appearing after a tone sounded. An important requirement is that a secondary task should affect the same working memory processing system (visual and/or auditory) as the primary task; otherwise, it may not be sensitive to changes in actual cognitive load. Dual-task techniques for measurement of cognitive load in multimedia learning were studied by Brünken, Plass, & Leutner (2003, 2004), Brünken, Steinbacher, Plass, & Leutner (2002), and Plass, Chun, Mayer, & Leutner (2003). The secondary task represented a simple visual-monitoring task requiring learners to react (e.g., press a key on the computer keyboard) as soon as possible to a color change of a letter displayed in a small frame above the main task frame. Reaction time in the secondary monitoring task was used as a measure of cognitive load induced by the primary multimedia instruction. The studies demonstrated the applicability of the dual-task approach to measurement of cognitive load experienced by each individual learner. Ratings of subjective mental effort associated with learning instructional materials have been used in many studies recently, as they are easy to implement and do not intrude on primary task performance. Furthermore, research indicates that subjective measures of mental load are reliable and correlate highly with objective measures (Moray, 1982; O'Donnell & Eggemeier, 1986). In addition to the usual dependent measures such as processing time, test performance, and practical task performance, subjective ratings of mental effort have been collected.

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Participants are usually asked to estimate how easy or difficult instructions were to understand by choosing a response option or a number on the scale, ranging from extremely easy (1) to extremely difficult (7 or 9). The scales are usually seven or nine point. Measures of subjective load and test performance scores have also been combined to generate instructional efficiency indicators calculated following Paas and van Merriënboer's (1993) procedure. This approach allows estimation of the relative efficiency of instructional conditions and the cognitive cost of instruction. High efficiency occurs under conditions of low cognitive load and high-level test performance, and low efficiency occurs under high cognitive load and low-level test performance. Efficiency values can be calculated, for example, by converting cognitive load and performance measures into z-scores (R and P) and combining z-scores using the formula: E=

P−R 2

The denominator 2 is used in this formula to allow an easy graphical interpretation of the efficiency of instruction by representing the cognitive load zscores (R) and performance z-scores (P) in a cross of axes. The relative efficiency of an instructional condition corresponding to a point (R, P) on the diagram can then be measured as the distance from this point to the line of zero efficiency (E = 0) and calculated using the above formula. The high efficiency area (relatively lower cognitive load with higher performance scores) with E > 0 is above the line E = 0. The low efficiency area (higher cognitive load with lower performance scores) with E < 0 is located below this line (Paas & Van Merriënboer, 1993). Using such efficiency indicators may help to eliminate, for example, the possibility that subjective ratings are merely measuring self-confidence or subjective comfort levels rather than cognitive load. If learners rate the mental effort of a task as low but perform well on tests (high efficiency), they are more likely rating cognitive load rather than just self-confidence.

EFFECTS GENERATED BY COGNITIVE LOAD THEORY The goal free and worked examples effects. According to cognitive load theory, means-ends analysis as a problem-solving strategy is associated with a significant extraneous cognitive load that has to be eliminated or reduced in order

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to facilitate learning. Evidence of interference between conventional problem solving and schema acquisition had been initially obtained in studies of solving puzzle problems (Mawer & Sweller, 1982; Sweller, Mawer & Howe, 1982). Confirmation was obtained from studies of mathematics and science problems (Cooper & Sweller, 1987; Owen & Sweller, 1985; Sweller & Cooper, 1985; Sweller, Mawer, & Ward, 1983). Lewis and Anderson (1985) also demonstrated that conventional goal directed problem solving could prevent learning of essential aspects of a problem's structure. The means-ends strategy involves different interconnected steps, such as defining differences between problem states, finding operators to reduce those differences, considering subgoals, etc., that might impose a significant cognitive load. This effect may outweigh any possible benefits of learning from direct problem solving (Gabrys et al., 1993). Sweller and his associates assumed that extraneous cognitive load could be reduced when novice learners' cognitive activities are directed to problem states and their associated moves (Owen & Sweller, 1985; Sweller & Levine, 1982; Sweller et al., 1983; Tarmizi & Sweller, 1988). The goal-free effect predicted a reduction of extraneous cognitive load and facilitation of learning by using goalfree or nonspecific goal problems. In such problems, the goal state is presented in nonspecific form (e.g., Calculate the values of as many parameters as you can instead of the traditional Calculate the value of the parameter X). A learner concentrates on each problem state and any move that will get her or him to a new problem state, and then the same applies to the next state, and so on. Because no activities irrelevant to schema acquisition are involved, cognitive load is reduced and learning is enhanced. This assumption was supported by a computational model (Sweller, 1988) and empirically confirmed in a variety of areas: puzzles, kinematics, geometry, and trigonometry. The goal-free groups demonstrated reduced acquisition time and errors, followed by superior performance on similar test and transfer problems. Analysis of verbal protocols, and written solutions showed that students presented with conventional problems during the acquisition stage continued to use the backward-working means-ends strategy on the test problems. Goal-free groups worked forward which provided evidence of acquired schemas (Sweller, 1989). Ayres (1993) demonstrated an increase in errors made by novices on the subgoal stage in solving simple two-move geometry problems (with only two unknown angles to be calculated) compared with the same problems in goal-free presentation. This stage effect was explained by an increase of cognitive load at the subgoal stage during application of means-ends analysis in conventional problem solving.

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A goal-free technique is highly effective for problems that have a limited search space. In areas of high search space, worked examples were suggested as an alternative to conventional problem-solving techniques. A worked example consists of a problem statement followed by all the appropriate steps to solution. The worked examples effect predicted a reduction of extraneous cognitive load and facilitation of learning by using relatively more worked examples instead of solving numerous conventional problems. Studying worked examples requires the learner to attend only to each problem state and its associated move. Empirical evidence for the effectiveness of worked examples for learning and their superiority over solving equivalent problems was obtained in multiple experiments performed by Sweller and Cooper (1985), and Cooper and Sweller (1987) using algebra transformation problems, such as, for example, for the equation b(a+c)/e=d, express a in terms of the other variables. Reduction in processing time during the acquisition stage in those experiments was followed by superior test performance by worked example groups. Similar results were obtained by Zhu and Simon (1987) who reported a number of experiments that compared subjects learning only from worked examples with those using a traditional lecture and problem-solving procedure. The research demonstrated that students studying worked examples learnt more quickly, being at least as successful as, and sometimes more successful than, students learning by conventional methods. Zhu and Simon (1987) also reported that the method of learning by examples had been successfully used in a Chinese school with a class covering the three-year curriculum in algebra and geometry in two years and at a slightly higher level of performance. Replacing conventional problems with completion problems may also reduce extraneous demand on working memory (completion problem effect, see van Merriënboer, 1990; Sweller, et al., 1998). A completion problem provides a partial solution that should be completed by the learners. The partial solution reduces the problem search space, focuses learner attention on problem states and their associated solution steps, thus decreasing extraneous cognitive load. Evidence of the effectiveness of worked examples and completion problems in comparison with conventional instruction in terms of solving transfer problems was also obtained by Paas (1992). His experiment in the area of statistics problems demonstrated that a cognitive structure resulting from instruction emphasizing practice with partly or completely worked-out problems is a more efficient knowledge base for solving transfer problems than one resulting from instruction based on conventional problem solving. Training that requires students to study worked-out problems leads to less effort-demanding and better transfer performance.

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Smith and Goodman (1984) demonstrated that hierarchical instructions containing explanatory schemas corresponding to steps of instruction (e.g., explaining what is the first subgoal, what is needed to accomplish this subgoal, and so on) improved understanding in comparison with linear instructions that contained only a linear sequence of steps after stating a general goal. Hierarchical instruction groups in the experiment (assembling an electrical circuit) showed more transfer, higher verbal recall and shorter reading times than the linear groups. Worked-out examples based on such explanatory schemas may provide a better instructional framework for representing chunks of steps and directing memory search (Smith & Goodman, 1984). Goal-free problems and worked examples both focus attention on problem states and their associated moves and reduce cognitive load. However, Sweller & Cooper (1985) did not observed a significant difference in performance on transfer problems between students learning from worked-examples and conventional problem-solving practice. By simplifying the task and providing more examples to study, Cooper & Sweller (1987) were able to obtain better transfer for the worked examples group. Extensive practice is required to automate problem-solving operators before any improvement can be observed for different problems. Automation frees up cognitive capacity, allowing the trainee to make appropriate generalizations. If transfer is an aim of instruction, an extensive mix of worked examples and actual problem solving could be the most effective instructional format (Cooper & Sweller, 1987; Gabrys et al., 1993). The split attention effect. Cognitive load theory predicted that studying worked examples could be superior to solving the equivalent problems because of a reduction in extraneous cognitive load. Germane or instructionally productive cognitive load caused by worked examples could be enhanced by adding processoriented information (the principled why and strategic how information) to examples of complex cognitive activities with multiple solution steps (van Gog, Paas, & van Merriënboer, 2004). However, there are situations when worked examples themselves require significant cognitive resources to be processed successfully. In some of these situations, cognitive load could be reduced by restructuring examples, for example, by breaking down explanations of complex solution procedures into smaller elements that can be learned separately (Gerjets, Scheiter, & Catrambone, 2004). In many situations, even such smaller explanatory modules may impose significant cognitive load. For example, in geometry, diagrams are usually accompanied by brief textual statements and neither text nor diagrams are intelligible in isolation. Such a worked example can be understood only by mentally integrating corresponding statements in the text and on the diagram and

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requires cognitive resources that are unrelated to learning. The imposed cognitive load may eliminate any benefit of a worked example. A series of experiments with circle geometry problems provided evidence for this hypothesis (Tarmizi & Sweller, 1988). Worked examples in a conventional format (i.e., text and diagram separate) demonstrated performance no better than solving conventional problems. The geometry worked examples required students to split their attention between diagram and text by searching and matching elements from the text to the appropriate entities on the diagram, and failed to facilitate schema acquisition and rule automation. Tarmizi and Sweller (1988) demonstrated that the search and match process involved with the geometry worked examples could be reduced if each textual statement was physically located near its matching entities on the diagram. Physically integrating textual information with the related diagram improved performance of the workedexamples group significantly. Similar results were obtained in experiments in kinematics (Ward & Sweller, 1990). Worked examples in kinematics usually consist of a problem statement followed by sets of equations representing the worked problem solution. The following example (Ward & Sweller, 1990) demonstrates a traditional worked example: A car moving from rest reaches a speed of 20 m/s after 10 seconds. What is the acceleration of the car? u = 0 m/s v = 20 m/s t = 10 s v = u + at a = (v - u)/t a = (20 - 0)/10 a = 2 m/ s

2

To understand the worked example, the learner had to mentally integrate the related sources of information and split her or his attention between the problem information and the worked solution. An experiment conducted under normal classroom conditions demonstrated that studying conventional kinematics worked examples was no more effective than solving the equivalent problems. Ward and Sweller (1990) found that the worked example effect took place when the conventional worked examples were reformatted so that the problem solution was

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integrated into the problem statement. For instance, the above example was transformed into the following integrated format: A car moving from rest (u) reaches a speed of 20 m/s (v) after 10 seconds (t): 2

[v = u + at, a = (v - u)/t = (20 - 0)/10 = 2 m/ s ]. What is the acceleration of the car? Learners who studied integrated worked examples processed the material quicker and made significantly less errors on test items compared to the conventional worked example group and the conventional problem-solving group. Physical integration of related sources of information (statements, diagrams, equations, etc.) decreases extraneous cognitive load by reducing search processes involved with conventional split source instructional formats. For example, in the case of instruction on operation of the Starter circuit for switching on/off a light by start/stop push buttons (Figure 3), an integrated instructional format is represented in Figure 5. 5. To cease operation of the light the stop push button is pressed. The circuit in the Starter is now open, the coil is no longer energized, and the switch returns to its normal open position.

2. Pressing down the start push button closes the circuit and allows the current to flow through the coil

Start

Stop A

N

1. The Starter consists of a start push button, a stop push button, and a switch activated by the coil.

switch

coil .

light 4. The light is operational, as the closed switch provides a closed circuit for it.

3. The energized coil closes the switch, which provides an alternative closed circuit for the coil to that provided by the start push button. The start push button now can be released without breaking the current flow through the coil.

Figure 5. Integrated diagram-and-text format of instruction on operation of the electrical circuit for switching on/off a light by start/stop push buttons.

In general, the split-attention effect occurs when instructional material requires learners to split their attention unnecessarily between multiple sources of information. Sweller, Chandler, Tierney, and Cooper (1990) obtained the effect

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using introductory teaching materials from coordinate geometry and computer programming. Novice learners who studied instructions with text integrated at appropriate points on the diagram spent less time processing the instructional material and exhibited superior test performance with faster solution times and fewer errors than learners who used conventional materials. Learners studying integrated instructional formats also demonstrated superior performance on transfer problems. Chandler and Sweller (1991) demonstrated the effect using biology materials in laboratory-based studies. One group received instructions on the blood flow through the heart, lungs, and body in a conventional split-source format. Textual explanations and the diagram depicting blood flow chains needed to be mentally integrated in order to be understood. Another group received the same information in an integrated format. The results of the experiment favored the integratedformat group. Despite spending less time studying the instructions, this group performed better than the conventional group on subsequent test problems. Superiority of integrated instructions over the conventional instructions was also demonstrated using introductory electrical engineering and Computer Numerical Control (CNC) programming materials (Chandler & Sweller, 1991; Chandler and Sweller, 1992). Sweller, Chandler, Tierney and Cooper (1990) extended their findings by integrating two sets of related textual information rather than integrating text and diagram. The instructions involved a series of commands for hand-operated machines and comparable commands for a computer numerical controlled system. To understand the conventional instructions, novice learners had to hold a segment of the ‘hand’ command text in working memory while searching for its matching numerical control command. The integrated instructions had the numerical control commands in brackets next to each hand-operated command. The results demonstrated that the integrated group's performance on test items was superior to the conventional group even though they spent less time processing the instructional materials. Chandler and Sweller (1992) replicated the effect with mutually referring sources of text. They provided evidence that the manner in which experimental papers in psychology are usually written results in split-attention between various segments of the paper for inexperienced readers (i.e., educational psychology students). If descriptions of an experimental group and a procedure (normally found in a Method section) are integrated with the results associated with that experimental group, the need for mental integration is eliminated and extraneous cognitive load is reduced. The inexperienced students gained advantage from the integrated version of a relatively simple report. The authors did not claim that the

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traditional format was inadequate. Findings could have been different if expert researchers, who know where to look for specific information, or reports that are more complex, had been used. A series of studies conducted by Mayer and his colleagues were related to the split-attention effect (Mayer, 1997). It was found that instructions consisting of separate text and unlabelled diagrams were less effective than diagrams that contained labels that clearly connected text and diagram (Mayer, 1989b; Mayer & Gallini, 1990). The labeled diagrams could be considered as a kind of physical integration of the diagram and text, as both techniques reduce the need to search. Research by Mayer and Anderson (1991, 1992) and Mayer and Sims (1994) on animated instructions and the contiguity principle may be viewed as a temporal example of the split-attention effect. The authors found that animation and related narration were most effective when presented simultaneously rather than serially. An integrated presentation of auditory and visual information was superior to their successive presentation. Thus, the split-attention effect has been tested with novice learners in a variety of areas in both laboratory and realistic training settings, with different types of related sources of information involved. Interestingly, physically embedded textual narratives have been used for many years in comic books for children, thus demonstrating their effectiveness in assisting children to comprehend complex materials (most reading materials are cognitively demanding for children). However, this technique was rarely used in general instructional materials until its cognitive efficiency had been investigated and appropriate recommendations suggested. A similar situation applies to the redundancy effect that is considered next. The redundancy effect. Research generated by cognitive load theory indicates that integrated instructional formats are beneficial for learning if the sources of mutually referring information need to be mentally integrated in order to be understood. However, physical integration of text and diagram may not always be appropriate. Often individual sources of information are self-contained, i.e. provide all of the required information in isolation. For example, electrical circuit diagrams might be intelligible without any reference to the accompanying text. Understanding such circuits might occur without processing the textual information. The elimination rather than integration of such redundant sources of information could be beneficial for learning. If the redundant information is integrated physically with essential information, learners have no choice but to process it. This imposes an extraneous cognitive load that interferes with the learning process.

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Thus, integration of all disparate sources of information is not always effective. Chandler and Sweller (1991) demonstrated the redundancy effect in the areas of electrical engineering and biology. When text and diagrams did not have to be mentally integrated in order to be understood, physically integrated instructions were no more effective than conventional instructions. In a series of laboratory experiments with electrical circuit instructions, novice learners who were not specifically asked to integrate disparate sources of information, required less instruction time and performed better than learners who were specifically instructed to integrate mentally related text and diagrams. One self-explanatory source of information was superior to two redundant sources of information in either a conventional, or an integrated format (Chandler & Sweller, 1991). These results were replicated in experiments with biology instructional material. The self-contained diagram of blood flow through the human body with arrows indicating the flow was used. Any additional statements placed on the diagram were redundant. Results showed that physically integrating such redundant text with the diagram interfered with learning. The integrated format was less effective than separate diagram and text. Removing the text entirely produced the best learning outcomes. Processing the redundant text requires additional cognitive resources and imposes an extraneous cognitive load. If a split-source format is used, students can reduce this load by ignoring the text when they realize it is redundant (Chandler & Sweller, 1991). Using a paperfolding task with primary school students, Bobis, Sweller, and Cooper (1993) demonstrated that diagrams (rather than textual explanations) could be redundant too. The redundancy and split-attention effects were investigated using various computer packages with novice learners (Sweller & Chandler, 1994; Chandler & Sweller, 1996). From a cognitive load perspective, the conventional method of instruction in software applications such as computer aided design/computer aided manufacture (CAD/CAM), word processing, and spreadsheet packages might not be efficient for learning. Instructions in conventional computer manuals require using the computer keyboard and simultaneously paying attention to information on the computer screen. The learners must split their attention when mentally integrating information from the manual, screen, and keyboard. This splitattention situation may result in a heavy extraneous cognitive load. Cognitive load theory suggests eliminating the computer during the initial instructional period and replacing it with diagrammatic representations of the computer screen and keyboard with segments of textual instructions integrated at their appropriate locations on the diagrams. Such modified integrated instructions are intelligible without reference to the computer: the computer appears to be

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redundant. A series of experiments involving an integrated manual only group, a conventional manual plus equipment group, and an integrated manual plus equipment group with materials in computer software applications and with electrical engineering (electrical installation testing) instructions demonstrated all the predicted effects with dramatic differences between the groups in both written and practical skills (Sweller & Chandler, 1994; Chandler & Sweller, 1996). For example, the manual on the CAD/CAM program used for the control of industrial machinery covered such basic operations as moving the cursor, using some basic menu functions, drawing lines, etc. The experiment included an instructional phase followed by written and practical tests. Despite spending less time studying their manuals, the integrated manual group was superior on most written and practical test items. The practical task results were of special interest in that study as the integrated manual only group had no previous experience with the computer prior to the testing phase. As discussed in the previous section, only when instructional material is characterized by a high degree of element interactivity and consequently may generate a heavy intrinsic cognitive load, an additional extraneous cognitive load caused by inappropriate design can be harmful to learning. In contrast, when information has a low intrinsic cognitive load due to low element interactivity, redesigning instructions to reduce extraneous cognitive load might not be as crucial. Estimates of element interactivity require assessing the number of elements that need to be processed concurrently, which in turn needs to consider the learner’s knowledge level. According to cognitive load theory, split-attention and redundancy effects take place only if learning materials are characterized by a high level of element interactivity. For example, learning to use a coordinate system in CAD/CAM packages imposed a heavy intrinsic cognitive load on inexperienced learners because they had to consider simultaneously different coordinates (Chandler, Waldron, & Hesketh, 1988; Hesketh, Chandler, & Andrews, 1988). However, such tasks as moving the cursor, using scales, grids, using some basic menu functions, could be learned quite independently and involve little interactions between elements. No split-attention and redundancy effects were demonstrated by Sweller and Chandler (1994) and Chandler and Sweller (1996) in areas of low element interactivity, and the format of presentation was not significant when using such materials. A modified self-contained manual format was beneficial only under conditions of high levels of element interactivity. For the other two formats in such conditions total cognitive load appeared to exceed the learners' available cognitive capacity. Measures of cognitive load using the dual-task method

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confirmed predicted differences in cognitive load in the Chandler and Sweller (1996) study. The secondary task consisted in recalling the previous letter seen on the screen of a separate computer while encoding the new letter appearing after a tone sounded. Strong primary and secondary task effects (better tests results with better recall of letters) favored an integrated modified manual group in areas of high element interactivity. No such effects were found in areas of low element interactivity. Thus, when instructional materials are intellectually demanding, the temporary elimination of the hardware may facilitate learning and reduce instruction time. Elimination of the manual and placing everything on the screen (as in computer-based training) may also be effective from the point of view of cognitive load theory, if there are no other sources of extraneous cognitive load (van Merriënboer & de Croock, 1992). However, in areas where motor components and spatial-motor coordination are essential (e.g., typing or driving a car), extensive practice with real equipment is always important (Sweller & Chandler, 1994). It was noted (Sweller, 1993; Sweller & Chandler, 1994) that forms of the redundancy effect had been demonstrated on a large number of occasions in the past. Reder and Anderson (1980) found that students could learn more from summaries of textbooks than from the full chapter. Miller (1937) found that presenting children with a word associated with a picture was less effective in teaching children to read than the word alone. Saunders and Solman (1984), Solman, Singh, and Kehoe (1992), and Wu and Solman (1994) demonstrated that the addition of pictures to words interfered with learning. Schooler and EngstlerSchooler (1990) found that having to verbalize a visual stimulus impaired subsequent recognition performance. The requirement to verbalize could be redundant and impose an extraneous cognitive load. Lesh, Behr, and Post (1987) found that mathematical word problems become more difficult with additional information in the form of concrete materials: processing these materials may impose an extraneous cognitive load. Holliday (1976) used a flow diagram to teach the nitrogen, water, oxygen and carbon dioxide cycles to high school students. One diagram represented the elements in the cycles as small pictures; another showed them as verbal labels. Students studied either one of the diagrams, or one of the diagrams alongside a text that presented the same material, or the text alone. On a multiple-choice verbal test of comprehension, students who studied the diagram only outperformed the other two groups. Students who were presented with text and diagrams performed no better than those who studied just text. The advantages of

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diagrams disappeared when they were used with text. Under these conditions, the text appeared to be redundant. The modality effect. Current theories of working memory consider capacities to be distributed over several partly independent subsystems, for example, separate auditory and visual modules (Baddeley, 1986; 1992; Penney, 1989; Schneider & Detweiler, 1987). For example, Baddeley (1986) proposed a model that includes three subsystems: a phonological loop, a visuospatial sketchpad, and a central executive. The phonological loop processes auditory information (verbal or written material in an auditory form), while the visuospatial sketchpad deals with visual information such as diagrams and pictures. Penney (1989) proposed a model of working memory (the "separate stream hypothesis") where the processing of auditory and visually presented verbal items was carried out independently by auditory and visual processors in working memory, and provided a considerable body of research in support of this hypothesis. Paivio's (1990) dual coding theory also suggests that information can be encoded, stored and retrieved from two fundamentally distinct systems, one suited to verbal information, the other to images. The two systems are interconnected and may contribute additively to memory performance. If information is coded in both the verbal and imaginal coding systems, memory for the information will be enhanced. Alternatively, if information is coded in only one of the two systems, the details will not be as easily recalled. According to cognitive load theory, the split-attention effect might occur when learners should mentally integrate two related sources of information and this integration overburden limited working memory capacity. When one of the sources is presented in auditory form, there still should be mental integration of the audio and visual information, but it may not overload working memory capacity if working memory is enhanced by a dual-mode presentation. The dualmode presentation does not reduce extraneous cognitive load but rather increases effective working memory capacity. The amount of information that can be processed using both auditory and visual channels might exceed the processing capacity of a single channel. Thus, limited working memory may be effectively expanded by using more than one sensory modality, and instructional materials with dual-mode presentation (for example, a visual diagram accompanied by an auditory text) can be more efficient than equivalent single modality formats. The modality effect occurs when separate sources of non-redundant information otherwise requiring integration are presented in alternate, auditory or visual, forms. Increasing effective working memory by using more than one sensory modality produces a

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positive effect on learning, similar to the effect of physically integrating separate sources of information. In a series of experiments using geometry instructional material, Mousavi, Low and Sweller (1995) found that a visually presented geometry diagram, combined with auditory presented statements, enhanced learning compared to conventional, visual only presentations. They also used audio/visual instructions where there was a written description of a geometry diagram rather than the diagrammatic format. The modality effect was not just limited to diagram and audio text, but also applicable to a written description accompanied by audio textual statements. Tindall-Ford, Chandler, and Sweller (1997) demonstrated that an audio text/visual diagram (or table) format of instructions in elementary electrical engineering was superior to purely visually based instructions. Measures of subjective mental load and instructional efficiency estimates were used to support the cognitive load interpretation of results. When separate instructions with low and high element interactivity materials were compared, strong performance differences were found in favor of an audio/visual format for the high element interactivity instructions. There were no differences between audio/visual and a visual only format for low element interactivity instructions. Jeung, Chandler, and Sweller (1997) demonstrated that dual-mode presentations only enhanced learning when an extensive visual search required for coordination of auditory and visual messages was eliminated. Mayer and his associates (Mayer, 1997; Mayer & Moreno, 1998; see also Clark & Mayer, 2003; Mayer, 2001 for overviews) have conducted a large number of experiments demonstrating the superiority of audio/visual instructions. For example, Mayer and Anderson (1991) presented information on how a bicycle tire pump works. There were four experimental conditions in this study. The first group viewed an animation depicting the operation of a bicycle tire pump and listened to simultaneous audio text, the second group was given only the audio text without the animation, the third group was provided only the animation with no audio component, and the fourth group received no formal training (control group). Results of this experiment (measured by number of creative and detailed solutions on the problem-solving test) showed that the first group outperformed the other three groups. Further research (Mayer & Anderson, 1992; Mayer & Sims, 1994) demonstrated that audio/visual instructions may only be superior when the audio and visual information is presented simultaneously rather than sequentially (the contiguity effect as an example of the temporal split-attention effect).

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Thus, according to cognitive load theory, learning might be inhibited when learners split their attention when mentally integrating text and graphics. However, when textual information is presented in auditory form, mental integration may not overload working memory that is now enhanced by using combined resources of the visual and auditory memories. Such a dual-mode presentation might be used to circumvent cognitive load problems caused by splitattention. Limited working memory may be effectively expanded by using more than one sensory modality. The amount of information that can be processed using both auditory and visual channels might exceed the processing capacity of a single channel. It might be especially appropriate when other forms of integration (e.g. physically integrated instructional formats) produce cluttered visual presentations. In fact, some techniques for effective expansion of working memory have been traditionally used in instructional practice. For example, written reminder notes used while performing simple arithmetic operations can be considered as a form of external memory that actually enhances working memory capacity. Schematic depiction of several sequential stages of a device operation in technical manuals allows readers to free working memory from keeping states of different parts of the device when tracing its operation. The success of dual mode instructional presentations in traditional education may possibly be partly attributed to the benefits of such presentations in optimizing working memory load. Novice students usually prefer listening to oral explanations of new complex diagram-based materials in geometry or engineering rather than reading such explanations in textbooks. In practice, however, many standard multimedia instructional presentations use auditory explanations simultaneously with the same visually presented text. From the point of view of cognitive load theory, such a dual-mode duplication of the same information using different modes of presentation increases the risk of overloading working memory capacity and might have a negative learning effect. When auditory explanations are used concurrently with the same visually presented text to explain a diagram, relating corresponding elements of visual and auditory content of working memory may consume additional cognitive resources. In such a situation, elimination of a redundant (duplicated) source of information might be beneficial for learning. Kalyuga, Chandler, & Sweller (1999) tested these suggestions with computerbased multimedia instructions on theoretical aspects of soldering. Three instructional formats were compared (visual text, audio text, and visual plus audio text) using participants without any substantial knowledge of soldering. A snapshot of the visual text instructional format is shown in Figure 6 (the fusion

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diagram). The results confirmed the advantage of dual-mode presentations for overcoming split--attention problems (audio text group outperformed visual text group). The audio text group demonstrated a lower number of reattempts at interactive exercises, a lower subjective rating of cognitive load and a higher test performance score than each of the other two groups. The results also demonstrated a disadvantage of dual-mode duplication of information (audio text group outperformed visual text plus audio text group). Inclusion of visually presented text simultaneously with the same text in an auditory form imposed an additional cognitive load on those learners who processed it. This result can be related to the work of Schooler and EngstlerSchooler (1990) which demonstrated that verbalizations of visual stimuli impaired subsequent recognition performance (the requirement to verbalize could be redundant, producing cognitive overload).

Figure 6. Snapshot of the visual-only format of instruction for the fusion diagram. Adapted from Kalyuga, Chandler, & Sweller (1999). Copyright © 1999 John Wiley & Sons, Ltd.

Mayer, Heiser, & Lonn (2001) compared performance of students who received a narrated animation explaining the formation of lighting storms and students who received the same narrated animation along with concurrent onscreen text. In two experiments, learners who received narration and animation performed better on tests of retention and transfer than learners who received

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animation, narration, and text. For learners who were required to coordinate and simultaneously process written and spoken text, an excessive working memory load could be generated. In a series of three experiments involving a group of technical apprentices, Kalyuga, Chandler, & Sweller (2004) compared the effects of simultaneously presenting the same written and auditory textual information as opposed to either temporally separating the two modes or eliminating one of the modes. The first two experiments demonstrated that non-concurrent presentation of auditory and visual explanations of a diagram proved superior in terms of ratings of mental load and test scores to a concurrent presentation of the same explanations when instruction time was constrained and system-controlled. The third experiment demonstrated that a concurrent presentation of auditory and visual forms of the same lengthy technical text (without the presence of diagrams) was significantly less efficient in comparison with an auditory-only text. The expertise reversal effect. The distinction between the split-attention and redundancy effects might be based on the distinction between sources of information that are intelligible or unintelligible in isolation. If a diagram and the concepts it represents are sufficiently self-contained and intelligible in isolation, then any text explaining the diagram is redundant and should be omitted in order to reduce cognitive load. Alternatively, if the concepts or functions of a diagram are not intelligible in isolation, then the diagram will require additional textual information. This information should be integrated into the diagram, or presented in auditory mode, in order to reduce cognitive load. However, intelligibility of information always depends on the level of expertise of the learner. For example, some information might not be intelligible in isolation for less experienced learners and so require physical integration with additional information to reduce an unnecessary working memory load. The same information may be intelligible in isolation for more experienced learners who have previously acquired schemas that allow all necessary inferences to be made. If additional instructional explanations are provided for more experienced learners, they are redundant and processing them may unnecessarily increase cognitive load. Eliminating redundancy may be the best way to reduce cognitive load in this situation. The split-attention effect might be replaced by the redundancy effect as expertise develops. Differences in learner knowledge base should be taken into account when analyzing the sources of cognitive load. What constitutes an element and which elements interact in learning entirely depends on a person's acquired schemas related to material being learned. Many interacting elements for one person may be a single element for another person with a sophisticated schema. For example,

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components of the Starter in Figure 3 (two push-buttons, and a switch) can act as many individual elements for novices, yet all these components may be considered together as a single element (the Starter) by an experienced electrical technician who has acquired the schema for the starter. If learners have sufficient knowledge to understand the circuit diagram, the textual explanations might be redundant for these learners. They may prefer to ignore the text but may have difficulty doing so if the text is integrated into the diagram (Figure 5), resulting in a higher cognitive load. In this situation, the best instructional format with the lowest unnecessary cognitive load for these learners may be a diagram alone format (the redundancy effect). On the other hand, if the circuit diagram is not intelligible in isolation, then additional necessary text should be presented in an integrated rather than a conventional split-source format (split-attention effect). Participants in most previously reviewed studies that originally demonstrated split-attention effect (e.g., Tarmizi & Sweller, 1988; Ward & Sweller, 1990; Sweller et al., 1990; Chandler & Sweller, 1991; 1992) did not have the higher order schemas for chunking interacting elements into single units. For these learners, the level of intrinsic element interactivity appeared to be high enough to make the total cognitive load overwhelming. In such situations, reduction of extraneous cognitive load caused by split-attention became critical. However, it was observed at the early stages of investigating the modality effect that differences between subjects in their domain-specific knowledge clearly influenced the effect. For example, Mayer and Gallini (1990) and Mayer and Sims (1994) found that only inexperienced novice students showed a strong contiguity (split-attention) effect and benefited from instructions that coordinated the presentation of verbal explanations and visual depictions. There were no improvements demonstrated for high-experience learners who were able to compensate for uncoordinated instruction by using their long-term memory knowledge. Kalyuga, Chandler, & Sweller (1997, 1998) demonstrated that the level of learner expertise is a critical learning condition that determines whether splitattention is a problem and relates split-attention to redundancy. Direct physical integration of text and diagrams was investigated in those studies. Fragments of textual explanations were directly embedded into electrical wiring diagrams similar to that in Figure 5. In the first experiment, instructional information was presented to learners with very limited experience in the domain. In this case, the split-attention rather than the redundancy effect was obtained. Students had great difficulty learning from a diagram alone. A diagram with associated text in a conventional, split-attention format was less difficult, and a physically integrated

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format was the most effective. Evidence that the effects were caused by cognitive load factors came from subjective rating scales. Students found the integrated diagram and text materials easier to process but performed at a higher level on the subsequent tests, resulting in substantially higher instructional efficiency measures using Paas and van Merriënboer's (1993) metric. Two subsequent experiments were designed to observe alterations in relative performance between the conditions as learners' level of expertise increased. These experiments tested the same learners who participated in the first experiment over a sufficient period to allow a substantial development of expertise. Experiment 2 used the same three conditions (however, with different, more complex wiring diagrams) as were used in Experiment 1. After extensive training in the domain, an interaction effect was obtained: the effectiveness of the integrated diagram and text condition decreased while the effectiveness of the diagram alone condition increased. Experiment 3 provided additional training to the point where substantial differences between an integrated diagram and text condition and a diagram alone condition were obtained, providing evidence of the redundancy effect. With experienced learners, the inclusion of the text interfered with learning. Students found the diagram alone materials easier to process but performed at a higher level on the subsequent tests, resulting in substantially higher efficiency ratings using Paas and van Merriënboer's (1993) metric. Subjective rating scales and efficiency data confirmed that the cognitive load profile of these two conditions was essentially the reverse of that obtained in Experiment 1 with novice learners (Figure 7).

Performance

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6

6

4

4

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Novices

Experts

diagram with integrated text diagram only

Figure 7. An interaction between instructional designs and levels of learner expertise in Kalyuga, Chandler, & Sweller (1998)

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Thus, the efficiency of an instructional design depends on levels of learner expertise in a domain, with trainees gaining optimal benefits from different formats at different levels of expertise. Similar patterns of results were obtained in other studies (Kalyuga, Chandler, & Sweller, 2000; 2001; Kalyuga, Chandler, Tuovinen, & Sweller, 2001; Tuovinen & Sweller, 1999). They will be reviewed in more detail in the following chapter. Collectively, these studies provided evidence for the existence of an interaction between instructional designs and levels of learner expertise in a domain, or the expertise reversal effect (Kalyuga, 2005; Kalyuga, Ayres, Chandler, & Sweller, 2003). The expertise reversal effect can be related to work on aptitude-treatment interactions (e.g., Cronbach & Snow, 1977; Lohman, 1986; Mayer, Stiehl, & Greeno, 1975; Shute, 1992; Snow, 1989, 1994; Snow & Lohman, 1984). Aptitude-treatment interactions (ATIs) occur when different instructional treatments result in differential learning rates depending on student aptitudes (knowledge, skills, learning styles, personality characteristics, etc.). In the expertise reversal effect, prior knowledge of students is the aptitude of interest. Procedures and techniques designed to reduce extraneous working memory load such as integrating textual explanations into diagrams to minimize splitattention, replacing visual text with auditory narration, or using worked examples to increase levels of instructional guidance, were found to be more efficient for less knowledgeable learners. With the development of expertise in a domain, such procedures and techniques often became redundant. If more knowledgeable learners could not avoid or ignore redundant sources of information, those sources were hypothesized to have imposed an additional cognitive load resulting in negative rather than positive or neutral effects. A cognitive load interpretation of the effect was supported by subjective rating measures of mental load. Knowledgeable learners found it more difficult to process instructional formats and procedures involving redundant components because of additional, unnecessary information that they had to attend to and integrate with their available schemas to find congruity between prior knowledge and incoming instruction. Studies of instructional design procedures of completion (van Merriënboer, 1990; 1997), scaffolding (van Merriënboer, Kirschner & Kester, 2003), and fading (Atkinson, Derry, Renkl & Wortham, 2000; Renkl, 1997; Renkl & Atkinson, 2003) supported the expertise reversal effect. These procedures provide novice learners with considerable support in the form of worked examples but gradually reduce this support as levels of expertise increase. Research by McNamara, Kintsch, Songer, and Kintsch (1996) can also be related to the expertise reversal effect. Using high school biology instructional

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materials, McNamara et al. (1996) found that additions to the original instructional text designed to increase text coherence benefited only lowknowledge readers. High-knowledge readers benefited from using the original text only, which was labeled by the authors as a minimally coherent format. These results can be interpreted from a cognitive load perspective. It could be suggested that the high-knowledge learners may not require any additional explanatory information as they found the original text intelligible without additional material and relatively low in cognitive load, thus exhibiting a redundancy effect. On the other hand, low knowledge learners required additional information to understand the original instructions. Thus, text coherence may be a function of expertise of the learners. Whereas the text may be minimally coherent for low knowledge learners and therefore require additional explanations, it may well be fully coherent in isolation for high knowledge learners.

LEVELS OF EXPERTISE AND OPTIMIZATION OF COGNITIVE LOAD IN INSTRUCTION Activities that learners are involved in during instruction alter as their levels of expertise change. In the absence of any external guidance, novices process unfamiliar information in working memory using mostly unorganized search procedures. Working memory must be (and it is likely it has evolved to be) limited to reduce the number of processing paths to manageable levels. In contrast, previously learned material, held in long-term memory by experts, is already appropriately organized, and there are no apparent working memory limits when experts deal with well-learned information according to their previously acquired schemas (Sweller, 2003, 2004). The acquisition of expertise is a complex multi-level process involving, on the one hand, continuous construction of new schemas, during which working memory limitations are severe, and on the other hand, the retrieval from long-term memory and application of previously acquired schemas, during which there are no discernible working memory limits. The coordination of the vastly different activities of schema construction and schema application should be taken into account as a major consumer of cognitive resources that requires an appropriate share of working memory capacity. Cognitive activities of novice learners are mostly directed towards construction of new schemas, while more knowledgeable learners retrieve and apply previously acquired schemas to the newly encountered situations within well-learned domains. If more knowledgeable learners are presented with

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instructions intended for schema construction purposes, those instructions may conflict with currently held schemas resulting in redundancy and expertise reversal effects. “...Less skilled students are most likely to benefit from direct instruction in how to construct a conceptual model for the to-be-learned material, whereas more skilled students are likely to already possess and spontaneously use sophisticated conceptual models that may conflict with models presented during instruction” (Mayer, 1989a, p. 44). The simultaneous use of different (schema-based and instruction-based) cognitive constructs when dealing with the same units of information may require knowledgeable learners to unnecessarily expend extra cognitive resources compared to instruction that relies more on pre-existing schemas for guidance. Cross-referencing and integration of related redundant components that many experienced learners may attempt to carry out might decrease cognitive resources available for meaningful learning or even cause a cognitive overload. This additional expenditure of cognitive resources may occur even if a learner recognizes the redundancy and tries to ignore the redundant information. Thus, instructional guidance, which may be essential for novices, may have negative consequences for more experienced learners, and it may be preferable to eliminate the instruction-based guidance for these learners. When a well-guided instruction is beneficial for novices (resulting in better performance compared to performance of novices who learned without such guidance) but disadvantageous for more experienced learners (resulting in poorer performance compared to performance of experts who learned from guidance-lean instruction), this is a case of the expertise reversal effect. For example, textual materials that were essential for novices in order to understand wiring diagrams in Kalyuga et al. (1998) (e.g., Figure 5), became redundant when presented to more experienced learners. Experts who acquired considerable high-level schemas in their area of expertise may not require any additional explanations. If explanations, nevertheless, are provided, processing this redundant information and integrating this information with available schemas may increase the load on limited capacity working memory. Processing redundant information that is not necessary because it either deals with issues already dealt with elsewhere or already known by the learner may cause an unnecessary working memory load hindering instructional outcomes. For experts, an instructional format with redundant material eliminated may prove superior to a format that includes the redundant material. A minimalist redundancy-free format may result in better learning with less cost in terms of instruction time and cognitive effort.

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Using appropriate instructional procedures and removing redundant activities at each level of learner expertise would minimize interfering or unnecessary cognitive load and increase proper or germane load. Such a process may be generally considered as a procedure of optimization of cognitive load in instruction at each stage of the acquisition of expertise in a domain. A perfectly optimized instructional sequence is schematically represented in Figure 8, which shows relative proportion of instruction-based and schema-based guidance at different levels of learner expertise. At each level of expertise, instructional guidance is provided for those (and only for those) components of information that are unfamiliar for learners at this level of expertise and could not be supported by preciously acquired schemas.

Instructionbased guidance

Schemabased guidance Novices

Experts

Figure 8. Optimized instructional sequence

In contrast, non-optimal instruction may have gaps corresponding to information that is supported by neither instructional guidance nor learner internal schematic knowledge structures (Figure 9). To make sense of this information, learners would have to apply cognitively expensive problem-solving search processes such as means-ends analysis. Non-optimal instruction may also have overlaps corresponding to information that is supported by both schematic knowledge structures and external instructional guidance. These sources of cognitive support then need to be cross-referenced and integrated by the learner in working memory resulting in an excessive cognitive load. For example, in studies of Kalyuga, Chandler, and Sweller (2001) and Kalyuga, Chandler, Tuovinen, and Sweller (2001), worked examples were cognitively optimal instructional formats for novice learners because they

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substituted for missing schemas by organizing information in working memory. At intermediate levels of expertise, a mix of detailed examples for supporting construction of higher level schemas not yet available and problem statements or diagrams for supporting retrieval of previously acquired lower level schemas could be optimal. At higher levels of learner knowledge, most cognitive activities are based on using previously acquired schemas that can organize knowledge elements in working memory. Activities designed to support construction of these schemas at high levels of expertise might be redundant and inefficient. Using the framework of the basic cognitive architecture (Figure 1), cognitive structures and processes for learners with different levels of expertise learning from instructions with different levels of guidance are graphically represented in Figures 10 - 13. When novices without appropriate higher level schemas in a domain learn from instruction with high level of guidance (a cognitively optimal situation), only some low-level schemas could be activated in long-term memory. All necessary executive support in constructing new knowledge comes from the external instructional guidance (red-colored components in Figure 10). If such guidance is not available (a cognitively non-optimal situation), novice learners have to engage in cognitively inefficient problem search processes that would results in less learning, if any (Figure 11).

Instructionbased guidance

Schemabased guidance Novices problem solving search

Experts cross-referencing of schemas and instructions

Figure 9. Non-optimized instructional sequence.

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WORKING MEMORY Executive function Constructing integrated mental representations of a current situation or task

Verbal information sub-system

Pictorial information sub-system

Lowlevel schemas

Lowlevel schemas New knowledge

LONG-TERM MEMORY Text

Pictures

INSTRUCTIONAL GUIDANCE

INSTRUCTIONAL PRESENTATION Figure 10. Cognitive structures and processes for novices learning from well-guided instruction.

WORKING MEMORY Executive function Verbal information sub-system

Constructing integrated mental representations of a current situation or task

Problem solving search

Lowlevel schemas

Pictorial information sub-system Lowlevel schemas

New knowledge

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Figure 11. Cognitive structures and processes for novices learning from non-guided instruction.

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WORKING MEMORY Executive function Verbal information sub-system Lowlevel schemas

Constructing integrated mental representations of a current situation or task Pictorial information sub-system

Highlevel schemas

Lowlevel schemas New knowledge

LONG-TERM MEMORY Text

Pictures INSTRUCTIONAL PRESENTATION

Figure 12. Cognitive structures and processes for experts learning from non-guided instruction.

However, if instruction with low level of guidance is presented to experienced learners (a cognitively optimal situation), their previously acquired high-level schemas might provide all necessary executive support in constructing new cognitive representations (blue-colored components in Figure 12). If experienced learners are provided with instruction with high level of guidance (a cognitively non-optimal situation), both external instructional guidance and previously acquired schemas would support constructing the same components of new knowledge. Corresponding cognitive units in working memory could partially overlap and conflict with each other (Figure 13). Thus, there are strong theoretical arguments based on cognitive load theory and accumulated empirical evidence that suggest that instructional techniques that are highly efficient with less experienced learners may lose their efficiency and even have negative instructional consequences when used with more experienced learners. Therefore, instructional techniques and procedures may need to change radically as learners acquire more expertise in a domain. Accordingly, optimization of cognitive load in instruction should assume not only presenting appropriate information at the appropriate time during continuing process of acquisition of expertise, but also the timely removal of redundant information as

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levels of learner expertise increase. Specific examples and empirical researchbased principles of optimizing cognitive load for more advanced learners will be described in the following chapter.

WORKING MEMORY Executive function Verbal information sub-system Lowlevel schemas

Constructing integrated mental representations of a current situation or task Pictorial information sub-system

Highlevel schemas

Lowlevel schemas New knowledge

LONG-TERM MEMORY

Text

INSTRUCTIONAL GUIDANCE

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INSTRUCTIONAL PRESENTATION Figure 13. Cognitive structures and processes for experts learning from well-guided instruction.

Chapter 4

COGNITIVE LOAD PRINCIPLES IN INSTRUCTIONAL DESIGN FOR ADVANCED LEARNERS ELIMINATING REDUNDANCY IN MULTIMEDIA INSTRUCTION Multimedia instruction refers to the instructional presentations that use both text (printed, on-screen, and/or spoken) and pictures (still or animated) (Mayer, 2001; 2005). It is assumed that information processing and the learning process are facilitated when the text is accompanied by pictures (Hegarty & Just, 1989; 1993; Levin & Mayer, 1993; Mayer, 1989b, 1993; Mayer & Anderson, 1992; Mayer & Sims, 1994). Comparative studies of textual and diagrammatic representations by Larkin and Simon (1987) showed that diagrams could be better representations not because they contain more information, but because they provide indexing of this information in a manner that is more cognitively efficient. In diagrams, information is organized by location. Most of the information needed at any time is presented at a single location, each element may be located beside any number of other elements, and little search is required. In a text, the information is indexed by the position, with each element neighboring only the next element in the list. Diagrams group together all information that is used, thus avoiding large amounts of search for the elements (Larkin & Simon, 1987). Studies in various domains have demonstrated that the instructional advantages of diagrams depend on student characteristics such as general ability, spatial reasoning, verbal ability, vocabulary, age, and gender (Winn, 1987). A general pattern is that high-ability students are usually in less need of explicit

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descriptions of the elements and their relations in graphic form than low-ability students. The presence of a diagram may help the less able students but not the high-ability students. Winn (1987) also noted that because there are limits to the amount of information about elements and their relationships that low-ability students can take in, the graphics might impose additional processing load for the low-ability students. However, he did not study effects of restructuring instructional materials to reduce this processing load, for example, effects of integration of textual instructions at their appropriate locations on the diagram. Prior knowledge has also been considered as an important factor contributing to individual differences in the effect of instruction based on text and visual displays (Schnotz, 2002). For example, when using graphics, more knowledgeable learners (university students) concentrated on information that was relevant to the construction of a mental model without calling upon textual information (Schnotz, Picard, & Hron, 1993). A number of studies of individual differences in learning from text and graphics demonstrated that the instructional advantages of diagrams depended on student domain-specific knowledge and experience. Acquired schemas allow more knowledgeable learners to avoid processing overwhelming amounts of information and reduce load on limited working memory. For example, experiments by Hegarty and Just (1989) demonstrated that high-mechanicalability learners comprehended both a text alone and a diagram alone at a higher level than low-mechanical-ability learners. It was assumed that high-mechanicalability learners were able to locate the relevant information in a diagram and were less dependent on an accompanying text. The way a text and a diagram are processed by high- and low-mechanical-ability learners depends on required cognitive effort. High-mechanical-ability learners might require less effort to construct a mental representation from either medium alone. Since switching between processing a text and processing a diagram requires considerable additional cognitive effort, these learners are able to reduce it by switching less often between the two media than low-mechanical-ability learners. The highmechanical-ability learners inspected the diagram rarely. They were able to hold its representation in working memory because it contained fewer chunks for them. Available schemas for mechanical systems made it less necessary for highmechanical-ability students to inspect the diagram in order to construct a representation (Hegarty, Just, & Morrison, 1988; Hegarty & Just, 1993). Lacking proper schemas, the low-mechanical-ability learners had to look at the diagram each time a new piece of textual information referred to the diagram. These learners switched often between the text and diagrams. Mental integration

Cognitive Load Principles in Instructional Design for Advanced Learners 71 of text and graphics occurred in small units that were manageable within the capacity of learners' working memory. Later these units were combined at a higher level, with the diagram used as an external representation that helped the learners to free up resources necessary for integration (Hegarty & Just, 1993). Glenberg and Langstone (1992) and Kieras (1992) also noted the role of a diagram as an external memory aid that frees up working memory resources of low-ability students while processing a text and a diagram. There are other forms of graphic representation of information that have been studied extensively, such as various types of cognitive maps (two-dimensional node-link diagrams), for example knowledge maps (Lambiotte, Skaggs, & Dansereau, 1993), concepts maps (Novak & Gowin, 1984), or semantic maps (Johnson, Pittleman, & Heimlich, 1986). Evidence has also been obtained that using cognitive maps is more beneficial to students with lower prior knowledge or verbal ability (Lambiotte & Dansereau, 1992; Rewey, Dansereau, & Peel, 1991). Cognitive maps may interfere with learning and would not be of much help when learners have already constructed schemas of the instructional material. When learners process both text and pictures, they have to mentally integrate verbal and pictorial representations in order to achieve understanding. The previous chapter described studies that demonstrated that when text and pictures are not synchronized in space (located separately) or time (presented after or before each other), the integration process may increase working memory load and inhibit learning due to cross-referencing different representations. Physically integrating verbal and pictorial representations may eliminate this split-attention effect (Chandler & Sweller, 1991; 1992; 1996; Mayer & Anderson 1991; 1992; Mayer & Gallini, 1990; Sweller, Chandler, Tierney, & Cooper, 1990; Tarmizi & Sweller, 1988; Ward & Sweller, 1990). Sections of written text could be embedded directly in the diagram in close proximity to relevant components of the diagram, and segments of narrated text could be presented simultaneously with the diagram (or relevant animation frames). When instructing learners who are more experienced in a specific domain, there could be a situation when the source of information (textual or pictorial) that is essential for a novice learner may be redundant for a more knowledgeable person. In the physically integrated format, processing the redundant information and integrating that information with the learner’s schemas could be difficult to avoid. Attending to and integrating redundant information with existing schemas requires a share of cognitive resources that becomes unavailable for the construction and refinement of new schemas. Therefore, in the case of more advanced learners, elimination of redundant verbal or pictorial information might be the optimal format of instruction.

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Research by Mayer and Gallini (1990) and Mayer, Steinhoff, Bower, and Mars (1995) in learning from text and illustrations demonstrated that well designed text-with-pictures instructional formats were more helpful for lowknowledge learners than for high-knowledge learners. With increases in learners’ knowledge in a domain, beneficial effects of such presentations disappeared. For example, these studies found that differences between subjects in their domainspecific knowledge clearly influenced the contiguity (or split-attention) effect when learning from text and pictures. Coordination of words and pictures improved problem-solving transfer for low-experience learners but not for highexperience learners. Supposedly, learners with a high level of domain-specific knowledge were able to compensate for uncoordinated instruction by retrieving relevant knowledge from long-term memory. Median effect size differences (the effect size for the high-knowledge learners subtracted from the effect size for the low-knowledge learners) were 0.60 for retention questions and 0.80 for transfer questions (Mayer, 2001). In experiments demonstrating the expertise reversal effect, Kalyuga et al. (1998) found that advanced electrical trainees learned relatively new instances of wiring diagrams in familiar domains significantly better from the circuit diagrams alone than from diagrams with embedded detailed textual explanations. Conservative estimates of effect sizes (using higher standard deviation values) were 0.55 for questions about circuits operation and troubleshooting, and 0.65 for diagram faultfinding questions. The advanced trainees also reported less mental effort when studying the diagram-only formats. The integrated textual explanations were clearly redundant for these learners and these explanations could not be avoided without a substantial cognitive effort. Dual-modality (e.g., combined auditory and visual) presentations have been shown to be an effective alternative to direct physical integration of text and diagrams in dealing with split-attention situations. Working memory capacity could be effectively increased by presenting a visual diagram with spoken rather than written explanations. According to the modality effect, novice learners can integrate textual explanations and pictures more effectively when the text is narrated rather than presented in an on-screen form (Mayer, 1997; Mayer & Moreno, 1998; Mousavi, Low, & Sweller, 1995; Tindall-Ford, Chandler, & Sweller, 1997). (Note, though, that presenting the same text simultaneously in written and spoken form still may generate an excessive working memory load, according to Kalyuga, Chandler, and Sweller, 2004). For high-knowledge learners, however, narrated explanations may become redundant and reduce learning efficiency. For example, when training inexperienced apprentices of manufacturing companies in reading different

Cognitive Load Principles in Instructional Design for Advanced Learners 73 cutting speed charts (nomograms) used to determine the appropriate number of revolutions per minute to run specific types of cutting machines, Kalyuga et al. (2000) demonstrated that replacing visual texts with corresponding auditory explanations was beneficial for the novice learners (modality effect). An onscreen animated diagram combined with simultaneously narrated detailed explanations on how to use the diagram (see Figure 14 for a snapshot of the instructional presentation) was the most efficient instructional format. When a novice trainee clicked on a particular step-heading button, an auditory narration of an explanation of this step was delivered through headphones instead of being displayed as an identical visual text next to the diagram. Diagram-only presentation (Figure 15) was the least efficient instructional format for these learners.

Figure 14. A snapshot of the multimedia instructional format for a cutting speed nomogram. Adapted from Kalyuga, Chandler, & Sweller (2000). Copyright © 2000 by the American Psychological Association, Inc.

When learners became much more experienced in using these diagrams, the advantage of auditory explanations on how to use a relatively new type of diagrams disappeared while the efficiency of the diagram-alone presentations increased. After additional training, when the trainees became more advanced in

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the domain, a substantial advantage of an animated diagram-only presentation over the diagram-with-audio-text condition was obtained. A conservative estimation of the effect size was 0.62 for questions requiring application of learned procedural steps for using a nomogram in different new task situations. Subjective mental effort ratings supported a cognitive load interpretation of the results (Figure 16).

Figure 15. A snapshot of the diagram-only instructional format for a cutting speed nomogram. Adapted from Kalyuga, Chandler, & Sweller (2000). Copyright © 2000 by the American Psychological Association, Inc.

USING PROBLEM-BASED AND EXPLORATORY LEARNING ENVIRONMENTS Problem-solving and exploratory (or discovery) learning environments are relatively unguided forms of instruction. Such instructions could be very cognitively demanding for novice learners because of a heavy working memory load and might result in poor learning outcomes. Studies reviewed in the previous chapter demonstrated that using appropriately designed worked examples may

Cognitive Load Principles in Instructional Design for Advanced Learners 75 eliminate this source of cognitive overload. However, as learner experience in a domain increases, solving problems or exploring relatively new tasks become more knowledge-based activities due to acquired domain-specific schemas. Processing a redundant worked example that fully describes a solution path and integrating this description with corresponding previously acquired solution schemas may impose a greater working memory load than just practicing in problem solving or learning in an exploratory environment. For more advanced learners, such practice or exploration activities may adequately facilitate further schema refinement and automation.

Performance

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6 4

4

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diagram with auditory text diagram only Figure 16. An interaction between instructional designs and levels of learner expertise in Kalyuga, Chandler, & Sweller (2000).

Interactions between levels of learner expertise and levels of instructional guidance were investigated in a series of longitudinal studies designed in accordance with the general experimental sequence represented by Figure 17. Actual experiments usually included more than two stages with intensive training sessions conducted between the stages to increase learner experience in a specific domain. The selected experimental domains were narrow enough to allow substantial increase in learner expertise in a relatively limited amount of time (a few weeks or months). On the other hand, the domains were expandable enough to allow a gradual increase in complexity of the tasks faced by the learners. Using this experimental design pattern, Kalyuga, Chandler, Tuovinen, and Sweller (2001) demonstrated that the superiority of computer-based worked examples of domain-specific procedures disappeared as trainees acquired more experience in a domain. In the first experiment, worked examples providing full instructional guidance and a problem-solving environment providing limited

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instructional guidance were compared (a) with inexperienced learners, (b) after two consecutive training sessions designed to increase the level of learner experience, and (c) after two further consecutive training sessions. The task domain was writing simple programmable logic controller (PLC) programs for relay circuits of different levels of complexity. PLCs are usually used to automate aspects of production line processing in manufacturing. The levels of task difficulty were controlled by varying the number of elements in the circuits.

STAGE 1

STAGE 2

Full guidance format

Full guidance format Performance test

Limited guidance format

NOVICES

Mental effort rating

Performance test

INTENSIVE TRAINING SESSIONS Limited guidance format

Mental effort rating

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Figure 17. Experimental sequence for studying interactions between levels of learner expertise and levels of instructional guidance.

In the problem-solving procedure, participants were presented a series of relay circuits, displayed one at a time, and required to compose a program for each circuit by dragging components of the program (commands and circuit element numbers) to appropriate positions in the program table (see Figure 18 for an example of a simple circuit and Figure19 for a more complex circuit). Trainees were given three attempts to get a correct answer with 3 min allowed for each attempt. An attempt ended when a student clicked on a “Check” button to allow the software to check the correctness of the answer. If the student failed to obtain the correct answer during those three attempts, the correct steps in programming the circuit were provided. At the beginning, simple circuits containing only three elements were presented. The following sets of circuits contained increasingly larger number of elements. The procedure for the worked-example group was identical to the problem-solving procedure except that it included examples of relay circuits with the programming steps embedded in these circuits (Figure 20).

Cognitive Load Principles in Instructional Design for Advanced Learners 77 In this group, participants were requested to follow mentally all the steps according to a numbered sequence.

Figure 18. A snapshot of the problem-solving practice in PLC programming (simple circuit).

Figure 19. A snapshot of the problem-solving practice in PLC programming (complex circuit).

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Figure 20. A snapshot of a worked example of writing PLC programs.

As learner experience in the domain increased, the relative improvement in performance of the problem-solving group was superior to the worked-example group. Nevertheless, a redundancy effect with a statistically significant superior performance by the problem-solving group was not obtained. It is possible that a redundancy effect could have occurred if more training had been provided to learners. However, because the experiments took place in natural training environments (apprentice training centers), the length of trainees' exposure to specific training materials was limited. It was expected that a redundancy effect might occur if the learners were trained in a related but less complex domain. In Experiment 2, the worked-examples and problem-solving instructional approaches again were compared first with less experienced learners and then after two consecutive training sessions. The task domain was writing Boolean switching equations for relay circuits of gradually increasing levels of complexity. By varying the number of elements in the circuits, it was possible to gradually increase the level of task difficulty throughout the experiment and observe continuous development of learner experience in the domain. Understanding the basics of PLC programming learned in Experiment 1 was helpful (similar relay circuits were used in both domains) but not sufficient for writing switching equations. Therefore, at the beginning of Experiment 2, the materials were more familiar to the participants than the materials at the beginning of Experiment 1. For the problem-solving procedure, participants were shown a series of relay circuits and requested to type in a Boolean equation for each circuit (Figure 21).

Cognitive Load Principles in Instructional Design for Advanced Learners 79 For the worked-example procedure, examples of the same relay circuits with the corresponding Boolean equation for each circuit were indicated to the learners (Figure 22). An expected redundancy effect with advanced learners was obtained in this experiment. Because the learners were sufficiently knowledgeable at the beginning of the experiment, worked examples were of no advantage in comparison with the problem-solving procedure. With additional training, worked examples became redundant resulting in a negative effect compared with problem-solving practice.

Figure 21. A snapshot of the problem-solving practice in writing switching equations.

Thus, in Experiment 1, a worked example effect was obtained with inexperienced learners but the superiority of worked examples disappeared with training. In Experiment 2, there was no worked example effect prior to extensive training sessions but eventually, with sufficient experience, learning relatively new tasks in the familiar domain was facilitated more by problem-solving practice than by studying worked examples (Figure 23). A conservative estimate of the effect size was 0.75 for questions requiring trainees to write switching equations for circuits with a larger number of components than that used during instruction. Exploratory (or discovery) learning environments usually provide even less instructional guidance than problem-solving practice, because the learners have to formulate the problem on their own before attempting to solve it. This form of instruction may impose a heavy working memory load on novice learners for exactly the same reasons as problem-solving practice. On the other hand, it could also be more beneficial than direct forms of instruction when used with advanced

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learners for whom the source of potential excessive cognitive load is eliminated with acquisition of appropriate schemas in their knowledge base.

Figure 22. A snapshot of the worked example on writing switching equations.

Figure 23. An interaction between instructional designs (worked examples vs. problem solving) and levels of learner expertise in Kalyuga, Chandler, Tuovinen, & Sweller (2001).

Cognitive Load Principles in Instructional Design for Advanced Learners 81

Figure 24. An interactive screen-based template. Adapted from Kalyuga, Chandler, and Sweller (2001). Copyright © 2000 Taylor & Francis.

Figure 25. A problem presented to learners after an acceptable circuit had been constructed.

Using the experimental sequence design represented in Figure 17, Kalyuga, Chandler, and Sweller (2001) compared worked examples-based instruction on how to construct switching equations for relay circuits with an exploratory learning environment. In the exploratory environment, learners were first required to construct their own circuits using an interactive screen-based template (Figure 24). Clicking on any thin contour gray line outside of symbols of input elements

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highlighted that line. Clicking on any contour symbol highlighted that input element. Clicking again on any highlighted element (a line or a symbol) eliminated highlighting. After an acceptable circuit had been constructed, the learners were invited to write a switching equation for this circuit (Figure 25). If a participant was repeatedly incorrect in her or his answers, the correct equation was provided. When the knowledge level of trainees was raised as a consequence of specifically designed computer-based training sessions using tasks with gradually increasing levels of difficulty, the exploratory group demonstrated better results than the worked examples group (Figure 26). A conservative estimate of the effect size was 0.33 for questions requiring trainees to select the correct switching equations for circuits with a larger number of components than that used during instruction. Subjective measures of mental effort supported the cognitive load interpretation of the effect. 8

Performance

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6 4 4 2

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worked examples exploratory learning Figure 26. An interaction between instructional designs (worked examples vs. exploratory learning) and levels of learner expertise in Kalyuga, Chandler, & Sweller (2001).

It should be noted that in this study, two levels of tasks were involved: simple tasks with few input elements and a very limited number of possible options to explore, and complex tasks with numerous options to explore. The exploratory learning was more beneficial than direct instruction for advanced learners only for the complex tasks. There were no differences between the procedures for the simple tasks. Similarly to other cognitive load effects, this effect occurred only when structurally complex instructional materials with high element interactivity

Cognitive Load Principles in Instructional Design for Advanced Learners 83 were used. Learning from such materials involved many interacting elements of information that had to be processed simultaneously in working memory potentially resulting in a heavy cognitive load. For relatively simple circuits, cognitive load was much lower and within the limits of working memory for either instructional format.

GRADUALLY REDUCING LEVELS OF INSTRUCTIONAL GUIDANCE Fully guided direct instruction is best represented by worked examples with detailed explanations of problem solution steps. For novice learners, properly designed worked examples were more beneficial instructional procedures than, for example, problem-solving learning on many occasions (Carrol, 1994; Cooper & Sweller, 1987; Paas, 1992; Paas & van Merriënboer, 1994a, 1994b; Quilici & Mayer, 1996; Rieber & Parmley, 1995; Sweller & Cooper, 1985; Trafton & Reiser, 1993). The studies reviewed in the previous section demonstrated that providing examples of worked-out solutions, while reducing cognitive load for novices, was redundant for more experienced learners. Additional cognitive resources were required by advanced learners to integrate the instructional guidance with available schemas that provided essentially the same guidance. Minimal guidance formats (problem-solving practice and exploratory learning environments) were cognitively optimal for these learners. Because the acquisition of expertise is a gradual process, the switch from fully guided instructional procedures to unguided problem-solving practice or exploration needs to be designed as a continuing process. Faded worked examples and completion assignments using the completion strategy (van Merriënboer, 1990; van Merriënboer et al., 2003) represent a suitable approach for instructional implementation of such gradual change in guidance. The completion strategy is based on a sequence of instructional procedures from fully worked-out examples with complete task solutions to conventional problems. Completion assignments contain a problem description, an incomplete worked-out solution, and tasks to complete. In a series of studies, Atkinson et al. (2000), Renkl (1997), and Renkl, Atkinson, and Maier (2000) demonstrated that the detailed worked examples, most effective for novices, should be gradually faded out with increased levels of learner knowledge, to be eventually replaced by problems. Renkl, Atkinson, Maier, and Staley (2002) and Renkl and Atkinson (2003) demonstrated the advantage of gradually reducing guidance with increases in expertise in

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comparison with an abrupt switch from worked examples to problems. In faded worked examples, as levels of learner knowledge in a domain increase, parts of worked examples are progressively replaced with problem-solving steps (Renkl, Atkinson, & Groβe, 2004). The method can be illustrated by an example from a computer-based tutor in solving elementary algebra equations (Kalyuga & Sweller, 2004). The tutor was designed as a series of worked examples, completion assignments, and conventional problems. The allocation of learners to appropriate completion assignments or stages of the faded worked examples was based on the outcomes of rapid diagnostic tests that are described in the second part of the book. The learners’ progress through the stages was also monitored by the diagnostic tests, and instruction was accordingly tailored to changing levels of expertise. Least experienced learners were initially presented with a series of fully worked-out examples (Figure 27), each followed by a problem-solving exercise. Depending on the results of a diagnostic test at the end of this phase, if necessary, some additional instructional materials were provided before proceeding to the next training stage. These instructions were designed as a set of shortened worked examples indicating only major steps without detailed explanations of intermediate procedures.

Figure 27. A fully worked-out example used in the computer-based algebra tutor.

Cognitive Load Principles in Instructional Design for Advanced Learners 85

Figure 28. A faded worked example used in the computer-based algebra tutor.

The second stage contained completion assignments (or faded worked examples) with the explanations of the last procedural step omitted. Learners were asked to complete the solution themselves (Figure 28). Each of the following training stages was similar to the previous one, except that a lower level of instructional guidance was provided to learners. In completion assignments, explanations of progressively more procedural steps were eliminated. The final stage contained only problem-solving exercises without any explanations provided. Another procedure that can be used to replace worked examples when instructing more knowledgeable learners was suggested by Cooper, Tindall-Ford, Chandler, and Sweller (2001). They found that imagining procedures and concepts might produce better instructional outcomes than simple studying of worked examples. Students were asked to imagine computer-presented worked examples on how to use a spreadsheet application (consisting of a set of diagrams with embedded textual explanations of sequential steps) rather than simply study the examples. High-knowledge students who had already acquired (at least, partially) schemas that allowed them to incorporate the interacting elements and support constructing relatively new representations, found the imagining technique more beneficial for learning compared with studying worked-out examples. Effect sizes in two sets of experiments were 1.24 and 0.75 for tests consisting of both similar and transfer problems.

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For low-knowledge students, on the other hand, the imagining procedure had a negative effect because these students had to process all components of instruction as individual elements in limited working memory. Worked examples effectively guided low-knowledge learners in constructing new schemas of complex procedures that contained many interacting elements. More advanced learners already had such schemas and studying the worked examples was a redundant activity for them. Thus, as learners acquire more expertise in a domain, studying worked examples in similar and repetitive situations could be gradually replaced with imagining corresponding procedures. This technique could provide additional practice for more advanced learners leading to a higher degree of schema automation. Similar results were obtained by Ginns, Chandler, & Sweller, (2003). A gradual reduction of levels of instructional guidance in exploratory learning environments can be accomplished by providing learners with less specified task goals or subgoals as the learners’ familiarity with the domain increases. If the learning goal in an exploratory environment is well specified, an ordered sequential structure of learning steps could be constructed, with the level of detail adjusted according to the levels of learner knowledge in the domain. Such instructional procedures could be suitable for relatively less advanced learners when lower level schemas are targeted by instruction. This procedure assumes systematic processing of all relevant knowledge components and acquisition of corresponding sub-schemas. To avoid working memory overload, all the relevant sub-schemas should be acquired one-by-one in advance to be readily available for retrieval when higher-level schematic knowledge structures are developed. In the case of higher-level schemas and poorly specified learning goals, it could be practically impossible to precisely structure appropriate ordered sequences of sub-tasks due to a large number of possible options and paths to explore. A learner (even a relatively advanced one) could be lost searching for relevant subgoals. This search might cause a heavy working memory load and consume cognitive resources that would become unavailable for constructing relevant higher order schemas. Therefore, an approach based on the acquisition of all potentially relevant sub-schemas before constructing higher order schemas (to reduce associated cognitive load) might require an excessive amount of time and effort to produce required learning results. On the other hand, irregular or random processing of lower level components, even though low in cognitive load, is also an unrealistic and unreliable approach with a low likelihood of producing required schematic knowledge structures in a reasonable amount of time. A more suitable exploratory approach for relatively advanced learners could be based on traversing an appropriately constructed

Cognitive Load Principles in Instructional Design for Advanced Learners 87 information space along several well-defined overlapping lines of representation. Instructional benefits of such multiple traversing might be partly due to an effective reduction of the number of relevant sub-schemas, which should be acquired before the required higher level schema could be developed, to a few overlapping contexts covering the information space. This approach is related to studies of Spiro and Jehng (1990) in cognitive flexibility. It was suggested that instruction in complex and ill-structured domains (e.g., literature) could be improved by using forms of nonlinear (or random access) learning that would allow exploring the domain by revisiting the same content material in a variety of different contexts. Spiro and Jehng (1990) assumed that a nonlinear multidimensional traversal of a complex instructional subject could support achieving learners’ cognitive flexibility, the ability to restructure one's knowledge for adaptation to a specific situation. This technique could be used in the design of cognitively efficient exploratory environments for advanced learners not only in ill-structured domains, but also in complex and well-structured domains in the case of poorly specified learning goals. When suggesting learners to explore several dimensions in an appropriately structured information space, we actually provide them with some overlapping instructional subgoals, thus preventing irrelevant activities that might overload working memory. Such a partially directed way of exploring the material by advanced learners could be more efficient for the acquisition of complex schemas than a prescribed linear sequence of learning or a fully unguided exploration. It could also be a means of a gradual reduction of instructional guidance. Providing pre-defined overlapping subgoals for advanced students who learn from complex exploratory environments might have the same cognitive load consequences as eliminating specific goals for novice learners in problem-solving situations in well-structured domains (goal-free effect; see Chapter 3). Both techniques could reduce cognitive load irrelevant to schema acquisition and facilitate learning. As an example of multidimensional representations in a complex information space, consider schematic structure of knowledge about technical objects described in Chapter 1 (Figure 2). The interconnected components, aspects, and levels of description of technical objects could be used as a general framework for constructing multidimensional representations of information in this area. It could be suitable for exploring by multiple traversing to facilitate the process of schema acquisition by advanced learners. In such an environment, a learner would be able to attend to any technical component, any aspect or level of its description, at any time and from any other location in the multidimensional space according to her or his needs, level of understanding, and preliminary knowledge.

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The possibility of an immediate access to information about functional, operational, and structural aspects of any technical component with different levels of specificity could be effectively realized in hypermedia learning environments by establishing an appropriate network of hyperlinks between different components, aspects, and levels of description. These hyperlinks would make any required modules of information easily accessible from any other aspect or level of description of any related object by single clicks on appropriate buttons or areas. For example, in a simple prototype of the suggested exploratory learning environment developed using the Authorware software, a network of hyperlinks between different components, aspects, and levels of description was created using navigational facilities of the authoring tool. The navigation panel, in addition to the set of standard pre-installed functions (back to home page, previous page, next page, word search, etc.), included a choice of functional (purpose), operational (operation), and structural (components) aspects of described objects, as well as two levels of the description specificity (overview and details). Different technical objects could be selected by clicking on their screen images. Having selected an object and a level of description, learners could inspect the functional, operational, and structural aspects of the object at the given level of specificity. Alternatively, having selected, for example, the functional aspect of description and a level of specificity, they could inspect the functional characteristics of different objects at the given level of specificity. Finally, having selected an object and an aspect of its description, the learners could inspect different levels of specificity of required information (‘zooming in’ or ‘zooming out’). Thus, the model could effectively provide a framework for multiple traversing of overlapping multidimensional representations of the technical information space. A specific way of traversing selected by a learner may depend on her or his specific level of expertise. Experts are able to efficiently interconnect different parts of their knowledge and switch between different levels of representations. Paris (1989) analyzed expert-novice differences in processing technical information and found that most technical texts fell into two groups. Texts for experts (e.g., adult technical encyclopedias, car manuals for mechanics) were organized around object subparts and their properties. Texts for novices (e.g., junior encyclopedias, car manuals for novices) focused on information about processes that allowed technical objects to perform their functions. The suggested user-model-based tailoring system was based on the assumption that the choice of strategy should be based on the user’s level of expertise (Paris, 1989). Likewise,

Cognitive Load Principles in Instructional Design for Advanced Learners 89 specific ways of traversing multidimensional exploratory learning environments may also need to be tailored to specific levels of learner expertise.

SUMMARY: TOWARD A COGNITIVELY EFFICIENT INSTRUCTIONAL TECHNOLOGY FOR ADVANCED LEARNERS Recent studies in cognition and instruction have provided a basis for the design and development of cognitively guided instructional systems. Such systems not only achieve desired instructional effects, but achieve them efficiently with optimal expenditure of resources (e.g., instruction time and mental effort). Cognitive efficiency is becoming an important feature of contemporary instructional systems. The change in research focus from the cognitive characteristics of tasks and learners to cognitively efficient ways of structuring and presenting instructional information is a shift from cognitive science toward a cognitive technology of instruction (Sweller, 1989). The studies reviewed and described in this part of the book represent an example of this move. Human cognitive capacity is limited: we can process only a very limited amount of information at any one time. The basic model of human cognitive architecture assumes a working memory with a limited capacity for maintaining unfamiliar information in an active state and a long-term memory with virtually unlimited storage capacity and duration. Working memory is the major human cognitive processor involved in constructing and integrating mental representations and in short-term maintenance of the relevant information. Longterm memory stores our organized knowledge base in the form of schemas. A schema contains information about some class of structures or objects and is directly related to our cognitive performance. Schemas are the basic units of knowledge representation that allow us to treat elements of incoming information in terms of larger higher-level chunks, thus reducing capacity demands on working memory. The main difference between

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experts and novices is in the way their knowledge base is structured and used. Working memory limits are of much less concern to experts who have their knowledge in the area of expertise well organized and stored in long-term memory. These knowledge structures significantly influence the content and characteristics of working memory and cause systematic differences among individuals in their working memory capacity for specific tasks. After sufficient practice, schemas become automated, and we are able to access our long-term knowledge base rapidly in an automatic manner. Rather than involving lengthy attention-demanding search, automated schemas require less working memory resources and allow information processing to occur with minimal mental effort. Cognitive mechanisms of schema acquisition and automation are foundations of our intellectual abilities and skilled performance. Well learned and developed schematic knowledge structures are major aspects of competent performance that allow efficient use of basic information processing features of human cognitive architecture. Studies of expert performance in a variety of domains indicate that experts can be characterized by efficient representations of problem situations in working memory, extensive domain-specific knowledge schemas in long-term memory, and efficient chunking mechanism of memory retrieval. Expert problem solving in knowledge-rich domains can be viewed as finding and adopting appropriate schemas in long-term memory. When solving specific problems, schema-driven experts spend more time on planning their steps, apply forward-working approaches, and use more efficient strategies of search. Experts in structurally complex domains possess multi-level hierarchical schemas representing classes of objects and situations. Constructing such schemas requires significant cognitive effort and begins with simplified representations. Experts integrate various levels of knowledge (intuitions, practical knowledge, theoretical knowledge; local and general knowledge) and switch between these levels while solving problems. Novice students cannot learn expert schemas directly. The instructional design process should be based on cognitive models of transition between different levels of expertise. Students' understanding of instructional materials is based on their available schemas. Resolving the conflict between a learner’s available schemas and conceptual models presented during instruction may require a significant mental effort and cause a negative learning effect. The backward problem-solving search strategies used by novices may also prevent learning due to working memory overload. Cognitive load is a major factor of complex cognitive performance and expertise acquisition. Many instructional materials and techniques may be ineffective because they ignore limitations of the human cognitive processing system and impose a heavy

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cognitive load. Cognitive load theory is based on the assumption that a person has a limited processing capacity, and proper allocation of cognitive resources is critical to learning. Schema acquisition and transfer from consciously controlled to automatic cognitive processing are the major learning mechanisms that reduce the burden on working memory. Using limited cognitive resources on activities not directly related to schema construction and automation (e.g., integration of information separated over distance or time, or processing redundant information) may inhibit learning. Learning materials with a high degree of element interactivity may impose a heavy intrinsic cognitive load on working memory. In this case, an appropriate instructional design that reduces extraneous cognitive load might be critical for efficient learning. Studies generated by cognitive load theory in realistic training settings and laboratory environments have demonstrated that learning can be significantly facilitated by restructuring instructional designs in a way that emphasizes procedures and activities directed towards schema acquisition and automation and places the primary cognitive burden on long-term memory schematic knowledge structures. For example, extraneous cognitive load for novice learners could be reduced when goal-free problems or worked examples are used instead of conventional problem solving, and when split-attention and redundancy situations are eliminated. The split-attention effect occurs when instructional material requires learners to unnecessary split their attention between multiple sources of information. Physical integration of the elements of information reduces extraneous cognitive load and enhances learning. The split-attention may also be eliminated if the information is presented in a partly audio and partly visual format because working memory capacity is enhanced under dual-modality conditions. If individual sources of information are self-contained, integration of the redundant information with essential information may impose an extraneous cognitive load that would interfere with learning. In this situation, the elimination rather than integration of redundant sources of information is beneficial for learning. All these effects should be of concern only when material has an intrinsically high level of element interactivity for the learner. Evaluation of cognitive load that might be imposed on prospective learners has to be an important part of the instructional design process. Cognitive load theory emphasizes that the influence of extraneous cognitive load on learning depends on the schemas that have been previously acquired by the learner and the level of automation of operations involved in the processing of instructional material. Novice learners require considerable assistance to understand new concepts. Therefore, introductory materials should include plenty of explanations

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and details, and they should be presented in a way that reduces unnecessary cognitive overload. Expertise in a domain decreases some of the limitations of working memory by enabling the use of organized schematic knowledge structures, stored in long-term memory, to process information more efficiently. However, in many instructional situations, expertise may also trigger additional cognitive load when learners are required to process redundant information. There is strong evidence that instructional techniques that are highly efficient with less experienced learners may lose their efficiency when used with more advanced learners (the expertise reversal effect). As levels of learner expertise in a domain increase, the relative changes in efficiency of instructional formats and procedures could be caused by the variations in working memory load involved in relating schema-based and instruction-based sources of cognitive support when constructing integrated mental representations of corresponding situations or tasks. A cognitive load explanation of the expertise reversal effect is based on the need for experts to cross-reference and integrate knowledge-based and redundant instruction-based cognitive structures dealing with the same units of information. An expertise reversal may be expected in situations when highly guided and integrated instructional presentations designed to assist novice learners are used with more advanced learners. Inappropriately used instructional formats and procedures could be very inefficient with advanced learners and may require an unnecessary additional expenditure of cognitive resources and instructional time. To be efficient, instructional techniques and procedures need to change significantly as learners acquire more expertise in a domain. Instructional implications of these findings for advanced learners are summarized below. Reducing extraneous and increasing germane cognitive load. In general, extraneous cognitive overload could be avoided by reducing diversion of cognitive resources on procedures and tasks that are not directly associated with learning (or schema acquisition). For example, eliminating the need to devote cognitive resources to searching and locating an appropriate fragment in a diagram and text or attending to unnecessary details could improve the learning efficiency for both novice and advanced learners. Computer-based instructional systems may also free up part of the learner’s cognitive load by carrying out some necessary subtasks, for instance, an information search, engaging on-line tools, referencing a dictionary, locating appropriate diagrams, simulating physical dismantling of equipment, etc. (Lajoie, 1993). There are instructional design principles and techniques that are specific for advanced learners only. Some of these principles are reverse to those intended for novice learners. For example, eliminating components of multimedia

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presentations (e.g., auditory explanations of a diagram) is recommended where they are redundant for advanced learners, even though this elimination may transform the multimedia instruction into a simple single-media format (e.g., diagram-only visual presentation). Exploratory and problem-based learning environments with minimal instructional guidance are recommended at higher levels of expertise, even though they are strongly discouraged when instructing novice learners. When dealing with learners at intermediate levels of expertise, a gradual reduction of instructional guidance is recommended. As learner levels of competency in a domain increase, completion tasks, faded worked examples, or suggested lines of exploration could be used to gradually change the levels of instructional guidance. It is reasonable to suggest that similar reversal effects could be demonstrated with other cognitive load reduction principles and methods as learners become more advanced in a domain. Further comprehensive studies are required of the effects of differing levels of learner expertise on alternative instructional procedures and techniques with various instructional materials and learning environments. Developing learner skills in managing cognitive resources. A suitable instructional design may not be the only means of optimizing learners’ cognitive resources. It could be supplemented by students’ metacognitive resourceallocation skills. For example, students could be advised to learn how to ignore redundant instructional explanations if they are sufficiently experienced in a domain. Advanced learners may spontaneously develop appropriate resourcemanagement skills as adaptive learning strategies (at least, the author spontaneously learned a long time ago to ignore on-screen text in the Top Ten section of the David Letterman’s Late Night Show). Learners’ metacognitive skills in managing cognitive load were not discussed in this book, as such skills need more focused research. Implementing cognitive design principles in computer-based learning environments. In the paper-based format, some cognitive load reduction techniques may produce rather cluttered instructional presentations. For example, embedding full or partial textual explanations into a diagram may obscure or distort components of the diagram. Consequently, unnecessary search processes may not be reduced to a degree that would facilitate learning. Computer-based multimedia instructional systems represent the best environment for the implementation of the described cognitive load design principles and techniques. In dynamic computer-based presentations, only relations and links corresponding to selected elements of the text or diagrams could be displayed on the screen when needed. For instance, arrow-directed embedded textual

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explanations with color-coded elements or pop-up on-demand windows could be shown on the screen when learners click on selected elements. In multimedia systems, combining on-screen diagrams with auditory on-demand explanations could be superior to conventional visual formats. Additionally, because the elimination of any redundant sources of information could be beneficial for advanced learners, experts can avoid verbal explanations that are unnecessary for them by turning off the auditory mode. Building learner-adapted instructional presentations. Advanced students, in contrast to complete novices, have been involved in learning a specific domain for some time. Determining the amount of instructional details or the level of instructional guidance relatively advanced students need is a difficult task. A commonsense assumption that additional details would not harm learning may be wrong. Instruction for more advanced students should represent a compromise. It should provide sufficient details enabling students to comprehend the material and omit redundant explanations that may create cognitive overload and hinder learning. A major instructional implication of the expertise reversal effect is that instructional design should be tailored to the intended learners’ particular levels of knowledge and skills related to the instructional area of study. Thus, cognitively oriented instructional materials should be directed towards certain groups of prospective learners, or materials should be adaptable to the levels of learner expertise. Computer-based instructional systems might include several different interaction modes presenting the same information in different ways to different learners. Moreover, the same information could be presented in different ways to the same learner at different times taking into account the development of her or his expertise. In learner-tailored instructional systems, an appropriate assignment of the learner to an instructional format could be guided by tracing the individual's learning performance (e.g., the number of reattempts during training exercises) or using appropriate assessment data. The empirical evidence described in this part of the book indicates that instructional designs and procedures that are cognitively optimal for less knowledgeable learners may not be optimal for more advanced learners. Instructional designers or instructors need to evaluate accurately the learner levels of expertise to design or select optimal instructional procedures and formats. Frequently, learners need to be assessed in real time during an instructional session in order to adjust the design of further instruction appropriately. Traditional testing procedures may not be suitable for this purpose. The following chapters describe a cognitive load approach to the development of rapid schemabased tests of learner expertise. The proposed methods of cognitive diagnosis will

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be based on contemporary knowledge of human cognitive architecture and will be further used as means of optimizing cognitive load in learner-tailored computerbased learning environments.

INDEX A ABC, 6 access, 1, 22, 25, 87, 88, 91 accuracy, 23 activation, 2 adaptation, 87 adult, 88 affect, 4, 41 age, 4, 69 aid, 71 alternative, 15, 32, 40, 44, 72, 94 alters, 15 American Psychological Association, 73, 74 application, 17, 37, 43, 61, 74, 85 aptitude, 60 arithmetic, 55 artificial, 8, 29, 32 artificial intelligence, 8, 29, 32 assessment, 95 assignment, 95 associations, 2, 32 atoms, 11 attention, 2, 3, 15, 16, 34, 35, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 55, 57, 58, 60, 71, 72, 91, 92 attention problems, 55 audio, 39, 53, 54, 55, 74, 92 automaticity, 33 automation, 31, 35, 40, 46, 75, 86, 91, 92

autonomous, 17 availability, 26, 30

B barrier, 11 behavior, 9, 15, 16, 30 beneficial effect, 72 benefits, 43, 55, 60, 87 biochemical, 11 biological, 11 biology, 48, 49, 50, 60 blood, 48, 50 blood flow, 48, 50 body, 48, 50, 53 bottleneck, 6 brain, 2 buttons, 37, 38, 39, 47, 57, 88 bypass, 24

C CAD, 50, 51 CAM, 50, 51 capacity, 1, 3, 4, 5, 6, 11, 17, 22, 28, 32, 35, 36, 40, 45, 51, 53, 55, 61, 62, 71, 72, 90, 92 carbon, 52 carbon dioxide, 52 causal relationship, 24

100

Index

central executive, 2, 3, 53 channels, 53, 55 children, 49, 52 Chinese, 44 chunking, 5, 6, 7, 58, 91 CIA, 6 classes, 9, 16, 32, 33, 91 classroom, 46 clusters, 10, 12, 13 coding, 53 cognition, 1, 2, 4, 7, 17, 28, 90 cognitive, vii, 1, 2, 3, 4, 7, 8, 11, 12, 13, 15, 16, 17, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 70, 71, 72, 74, 75, 80, 82, 83, 86, 87, 90, 91, 92, 93, 94, 95 cognitive activity, 39 cognitive capacity, 17, 45, 51, 90 cognitive effort, 39, 62, 70, 72, 91 cognitive flexibility, 87 cognitive function, 11 cognitive load, vii, 15, 23, 30, 32, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 63, 66, 74, 80, 82, 83, 86, 87, 92, 93, 94, 95 cognitive map, 33, 71 cognitive models, 29, 91 cognitive performance, 34, 90, 91 cognitive process, 4, 7, 11, 15, 17, 23, 24, 26, 31, 32, 35, 37, 39, 90, 91 cognitive processing, 11, 17, 23, 24, 35, 37, 39, 91 cognitive psychology, 8, 16 cognitive representations, 66 cognitive research, 29, 32 cognitive science, 29, 90 cognitive system, 1, 22 cognitive tasks, 2, 3 coherence, 61 coil, 37, 39, 40 communication, 35 competency, 21, 32, 94 complex systems, 29

complexity, 30, 36, 75, 76, 78 components, 2, 3, 7, 8, 9, 10, 11, 12, 14, 22, 25, 27, 28, 29, 30, 32, 33, 37, 39, 40, 52, 57, 60, 62, 63, 64, 66, 71, 76, 79, 82, 86, 87, 88, 93, 94 composition, 9 comprehension, 5, 6, 12, 52 computer, vii, 16, 29, 41, 47, 48, 50, 51, 52, 55, 75, 82, 84, 85, 94, 96 computer mouse, 41 computer software, 50 concentrates, 43 concept map, 13, 32 conceptual model, 29, 30, 34, 62, 91 concrete, 28, 52 conditional rules, 7 confidence, 29, 42 configuration, 8, 13, 37, 39 conflict, 34, 62, 66, 91 Congress, iv conservation, 22 constraints, 6 construction, 23, 36, 61, 64, 70, 71, 92 consumption, 35 context, 3, 11, 29, 30 contiguity, 49, 54, 58, 72 continuing, 66, 83 control, 2, 3, 17, 18, 27, 31, 48, 51, 54 control group, 54 controlled, 3, 28, 48, 57, 76, 92 coordination, 3, 52, 54, 61 covering, 44, 87 cues, 12, 22, 31 culture, 16 curriculum, 44 cycles, 15, 52

D declarative knowledge, 13, 17, 28 declarative memory, 8 degree, 6, 8, 33, 36, 38, 39, 40, 51, 86, 92, 94 demand, 30, 40, 44, 95 designers, vii, 32, 95 deviation, 72

101

Index diagnostic, 84 distortions, 12 division, 9 domain, 5, 6, 7, 9, 12, 16, 17, 18, 22, 23, 24, 25, 26, 27, 28, 30, 34, 38, 58, 59, 60, 63, 64, 66, 70, 71, 72, 74, 75, 78, 79, 84, 86, 87, 91, 93, 94, 95 domain-specificity, 26 duplication, 55 durability, 1, 2 duration, 90 duties, 29

E ears, 8 education, 55 educational psychology, 48 elaboration, 33 election, 12 electric circuit, 30 electric circuits, 30 electrical, 36, 37, 39, 40, 45, 47, 48, 49, 50, 54, 58, 72 electronic, iv, 8, 21 electronics, 22, 28 electrostatic, iv emergence, 11 encapsulated, 39 encoding, 6, 7, 11, 41, 51 energy, 22 engineering, 29, 32, 48, 49, 50, 54, 55 environment, 30, 75, 81, 86, 87, 88, 94 equipment, 50, 52, 93 evidence, vii, 14, 15, 24, 27, 37, 43, 44, 46, 48, 59, 60, 66, 93, 95 evolution, 12, 18 executive function, 3 executive functioning, 3 exercise, 84 experimental condition, 54 experimental design, 75 expert, iv, 6, 16, 17, 18, 19, 22, 24, 26, 27, 28, 29, 30, 32, 33, 34, 39, 48, 88, 91 expert systems, 29

expertise, vii, 1, 6, 16, 18, 21, 22, 26, 27, 28, 29, 34, 38, 39, 57, 58, 59, 60, 61, 62, 63, 64, 66, 72, 75, 76, 80, 82, 83, 84, 86, 88, 91, 93, 94, 95 experts, 6, 16, 18, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 39, 61, 62, 66, 67, 88, 91, 93, 95 exposure, 22, 78 eyes, 2, 8

F failure, 12, 23 false, 7 fatigue, 4 FBI, 6 feedback, 17 fire, 15 flexibility, 87 flow, 37, 40, 48, 50, 52 folding, 50 Ford, 54, 72, 85 foreign language, 24 functional aspects, 3 furniture, 2 fusion, 55, 56

G gender, 69 gene, 8, 45 general knowledge, 91 generalization, 9 generalizations, 8, 45 genetics, 21 goals, 16, 33, 39, 86, 87 GPS, 14 graph, 10 grids, 51 grouping, 2, 6 groups, 6, 11, 22, 25, 43, 44, 45, 51, 52, 54, 55, 88, 95 guidance, 23, 60, 61, 62, 63, 64, 66, 75, 76, 79, 83, 85, 86, 87, 94, 95

102

Index

H handling, 3 harm, 95 harmful, 27, 40, 51 hearing, 12 heart, 48 heuristic, 14 high school, 52, 60 high-level, 11, 23, 24, 28, 42, 62, 66 Holland, 30 human, vii, 1, 2, 4, 7, 8, 11, 13, 14, 16, 18, 21, 22, 31, 35, 50, 90, 91, 96 human cognition, 1, 2, 4, 7 human information processing, 11, 35 hypermedia, 88 hypothesis, 4, 5, 46, 53

I ideas, 7, 30 images, 3, 8, 30, 40, 53, 88 implementation, 83, 94 in situ, 93 inclusion, 59 indexing, 69 indicators, 32, 42 individual differences, 4, 5, 7, 70 induction, 14 industrial, 51 inferences, 57 influence, 5, 16, 18, 24, 27, 37, 91, 92 information processing, 11, 21, 24, 35, 69, 91 initial state, 14, 18 injury, iv input, 81, 82 instruction, vii, 12, 18, 26, 28, 29, 31, 32, 33, 34, 36, 39, 41, 42, 44, 45, 47, 50, 52, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 71, 72, 74, 79, 81, 82, 83, 84, 86, 87, 90, 91, 93, 94, 95 instruction time, 50, 52, 57, 62, 90 instructional design, vii, 18, 32, 34, 36, 40, 41, 59, 60, 75, 80, 82, 91, 92, 93, 94, 95

instructional materials, 32, 35, 37, 39, 41, 48, 49, 52, 53, 61, 70, 82, 84, 91, 94, 95 instructional practice, 55 instructional procedures, vii, 15, 63, 83, 86, 94, 95 instructional techniques, 13, 66, 93 instructional time, 93 instructors, vii, 95 integration, 3, 37, 39, 40, 47, 48, 49, 53, 54, 57, 58, 62, 70, 71, 72, 92 intellectual skills, 31 intelligence, 8, 29, 32 interaction, 36, 37, 59, 60, 75, 80, 82, 95 interaction effect, 59 interactions, 4, 39, 51, 60, 76 interactivity, 36, 37, 38, 39, 40, 51, 54, 58, 82, 92 interest, 51, 60 interference, 7, 37, 43 interpretation, 5, 42, 54, 60, 74, 82 interviews, 32 intrinsic, 36, 37, 39, 40, 51, 58, 92 isolation, 37, 45, 49, 57, 58, 61

J judgment, 7

K kinematics, 43, 46 knowledge, 1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 44, 51, 55, 57, 58, 60, 61, 63, 64, 66, 70, 71, 72, 75, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 95, 96

L language, 5, 6, 24, 36 language skills, 5 laws, 23, 29

Index learners, vii, 17, 18, 25, 27, 30, 33, 36, 37, 40, 41, 42, 44, 47, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 70, 71, 72, 73, 74, 75, 76, 78, 79, 81, 82, 83, 84, 85, 86, 87, 88, 90, 92, 93, 94, 95 learning, vii, 1, 5, 11, 12, 15, 16, 17, 18, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 64, 65, 66, 67, 69, 70, 71, 72, 74, 79, 81, 82, 83, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96 learning efficiency, 72, 93 learning environment, vii, 30, 32, 74, 79, 81, 83, 86, 88, 89, 94, 96 learning outcomes, 50, 74 learning process, 16, 31, 49, 69 learning styles, 60 learning task, 32 likelihood, 86 limitations, 3, 6, 7, 11, 24, 31, 35, 61, 91, 93 linear, 9, 11, 13, 33, 45, 87 links, 9, 13, 94 listening, 55 literature, 87 location, 69, 87 long period, 26 longitudinal studies, 27, 75 long-term, 1, 2, 5, 7, 8, 11, 12, 13, 16, 18, 21, 23, 24, 26, 31, 58, 61, 64, 72, 90, 91, 92, 93 long-term memory, 1, 2, 5, 8, 11, 12, 13, 16, 18, 21, 23, 24, 26, 31, 58, 61, 64, 72, 90, 91, 92, 93 low-level, 42, 64 lungs, 48

M machinery, 51 machines, 9, 48, 73 magnetic, iv maintenance, 3, 90 management, 94 manipulation, 2, 3 manufacturing, 72, 76 manufacturing companies, 72

103

mapping, 32, 33 Mars, 72 mass, 29 mathematical, 3, 27, 52 mathematics, 43 meaningful tasks, 5 meanings, 29 measurement, 22, 41 measures, 5, 41, 42, 59, 60, 82 mechanical, iv, 21, 70 mechanics, 22, 88 media, 70, 94 medical student, 27 memory, 1, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 15, 16, 17, 18, 21, 22, 23, 24, 25, 26, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 44, 45, 48, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 66, 70, 71, 72, 75, 79, 83, 86, 87, 90, 91, 92, 93 memory capacity, 5, 6, 11, 22, 35, 36, 40, 53, 55, 61, 72, 91, 92 memory performance, 53 memory retrieval, 91 mental load, 35, 41, 54, 57, 60 mental model, 7, 11, 33, 70 mental processes, 31 mental representation, 1, 3, 70, 90, 93 mental simulation, 25 messages, 54 metacognitive, 17, 18, 94 metacognitive skills, 18, 94 methodology, 22, 30, 32 metric, 59 misconceptions, 27, 32 modality, 52, 53, 54, 55, 58, 72, 73, 92 mode, 53, 54, 55, 57, 95 modeling, 18 models, 1, 2, 3, 7, 11, 13, 19, 26, 29, 30, 33, 34, 41, 62, 91 modules, 45, 52, 88 molecules, 11 momentum, 22 monitoring, 41 motor coordination, 52 mouse, 41

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Index

mouth, 8 multidimensional, 87, 88, 89 multimedia, 41, 55, 73, 93, 94, 95 music, 12

N narratives, 49 natural, 78 needs, 2, 31, 51, 83, 87 negative consequences, 62 nerve, 11 network, 88 New York, iii, iv nitrogen, 52 node, 71 nonlinear, 87 non-linear, 11 normal, 40, 46

O on-line, 93 operator, 14 optimization, 63, 66 oral, 55 organization, 1, 11, 21, 25, 33 overload, 15, 37, 53, 54, 56, 62, 75, 86, 87, 91, 93, 95 oxygen, 52

P paper, 48, 50, 94 parameter, 43 Paris, 88 pathways, 15 perception, 24, 26, 27 perceptions, 27, 28 perceptual learning, 28 performance, 2, 3, 4, 5, 6, 7, 13, 16, 17, 18, 21, 22, 24, 25, 26, 27, 28, 30, 31, 32, 33,

35, 41, 42, 43, 44, 45, 46, 47, 48, 52, 53, 54, 55, 56, 59, 62, 78, 90, 91, 95 personality, 60 personality characteristics, 60 perspective, 11, 50, 61 phone, 2 phonological, 3, 53 physics, 16, 21, 22, 24, 29 planning, 25, 91 plants, 30 PLC, 76, 77, 78 poor, 74 potatoes, 1 practical knowledge, 91 predictors, 5 pre-existing, 62 preparation, iv primary school, 50 primitives, 32 principle, 12, 26, 49 prior knowledge, 3, 6, 11, 12, 32, 60, 71 problem space, 14 problem-based learning, 94 problem-solver, 18 problem-solving, 1, 13,14, 15, 16, 17, 18, 22, 23, 24, 25, 27, 31, 32, 35, 40, 42, 43, 44, 45, 47, 54, 63, 72, 75, 76, 77, 78, 79, 80, 83, 84, 85, 87, 91, 92 problem-solving skills, 15 problem-solving strategies, 22, 25 procedural knowledge, 16, 29 procedural memory, 8 procedures, vii, 9, 12, 15, 16, 17, 23, 24, 25, 26, 28, 30, 32, 33, 45, 60, 61, 63, 66, 75, 82, 83, 84, 85, 86, 92, 93, 94, 95 production, 7, 8, 13, 14, 15, 16, 30, 76 program, 30, 51, 76 programming, 16, 21, 30, 47, 48, 76, 77, 78 property, iv proposition, 7 propulsion, 30 protocols, 43 prototype, 88 psychological, 11 psychologists, 22

105

Index psychology, 8, 16, 48

S R

random, 2, 6, 22, 23, 86, 87 random access, 87 random numbers, 2 range, 2, 4 rating scale, 58, 59 ratings, 41, 42, 57, 59, 74 reaction time, 41 reading, 3, 5, 6, 22, 45, 49, 55, 72 reading comprehension, 5, 6 real time, vii, 30, 95 reasoning, 26, 27, 32, 69 recall, 2, 5, 6, 7, 12, 22, 29, 45, 52 recalling, 3, 11, 12, 22, 41, 51 recognition, 13, 24, 31, 52, 56 reduction, 12, 14, 23, 43, 44, 45, 58, 86, 87, 94 redundancy, 49, 50, 51, 52, 57, 58, 59, 61, 62, 78, 79, 92 refining, 25 reflection, 30 Reimann, 16, 18, 22, 24, 25 rejection, 25 relationship, 27 relationships, 8, 24, 28, 31, 32, 37, 70 reliability, 9 remembering, 12, 30, 39 research, 4, 8, 13, 16, 29, 32, 41, 44, 53, 54, 67, 90, 94 researchers, 48 resources, 3, 4, 5, 15, 27, 28, 35, 39, 40, 45, 50, 54, 55, 61, 62, 71, 83, 86, 90, 91, 92, 93, 94 restructuring, 18, 45, 70, 92 retention, 33, 56, 72 retrieval, 6, 7, 11, 22, 61, 64, 86 returns, 40 risk, 55

scaffolding, 33, 60 schema, vii, 8, 9, 11, 12, 13, 14, 16, 18, 22, 23, 24, 25, 27, 28, 31, 35, 36, 38, 39, 40, 43, 46, 57, 61, 62, 63, 75, 86, 87, 90, 91, 92, 93, 95 schemas, 7, 8, 11, 12, 13, 14, 15, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 34, 38, 39, 43, 45, 57, 58, 60, 61, 62, 63, 64, 66, 70, 71, 75, 80, 83, 85, 86, 87, 90, 91, 92 school, 44, 50, 52, 60 science, 16, 27, 29, 43, 90 scores, 42, 57 search, 14, 18, 23, 24, 39, 40, 44, 45, 46, 47, 49, 54, 61, 63, 64, 69, 86, 88, 91, 93, 94 searching, 16, 25, 46, 48, 86, 93 second language, 36 selecting, 15, 22, 33 self, 17, 18, 26, 27, 36, 42, 49, 50, 51, 57, 92 self-confidence, 42 self-monitoring, 18, 26 semantic, 7, 71 semantic networks, 7 sensory memory, 3 sensory modality, 53, 55 sentence comprehension, 7 sentences, 3, 5, 7, 25, 39 sequencing, 33 series, 7, 46, 48, 49, 50, 53, 57, 75, 76, 78, 83, 84 services, iv short supply, 5 short-term, 1, 2, 4, 22, 90 short-term memory, 1, 22 signals, 35 similarity, 12 simulations, 25, 30 skill acquisition, 28 skilled performance, 16, 18, 21, 91 skills, 5, 8, 15, 17, 18, 26, 31, 32, 33, 51, 60, 94, 95 software, 32, 50, 76, 88 solutions, 18, 23, 43, 54, 83 spare capacity, 35

106

Index

spatial, 8, 52, 69 specific knowledge, 6, 7, 16, 25, 26, 28, 33, 58, 70, 72, 91 specificity, 26, 27, 88 speed, 1, 6, 26, 46, 47, 73, 74 spelling, 1 stages, 15, 17, 55, 58, 75, 84, 85 standard deviation, 72 standard model, 2 statistics, 44 stimulus, 52 STM, 2, 3, 6, 7 storage, 3, 4, 5, 6, 7, 90 storms, 56 strategic, 45 strategies, 17, 22, 25, 26, 28, 29, 33, 91, 94 strength, 1, 11 structuring, 90 student characteristics, 69 students, 9, 12, 18, 27, 29, 30, 34, 43, 44, 45, 46, 48, 50, 52, 55, 56, 58, 60, 62, 69, 70, 71, 85, 86, 87, 91, 94, 95 subjective, 39, 41, 42, 54, 55, 58, 60 subtasks, 93 summaries, 13, 52 superiority, 44, 54, 75, 79 supply, 5, 35 surface component, 27 surface structure, 23 switching, 37, 38, 47, 70, 78, 79, 80, 81, 82 symbols, 7, 24, 81 syntax, 36 systematic, 5, 86, 91 systematic processing, 86 systems, 3, 7, 8, 13, 15, 28, 29, 30, 53, 70, 90, 93, 94, 95

T task demands, 28 task difficulty, 76, 78 task performance, 6, 41 teachers, 32 teaching, 29, 30, 47, 52 technician, 58

technological, 9 technology, 90 telephone, 2, 29 temporal, 49, 54 test items, 47, 48, 51 test scores, 57 textbooks, 52, 55 theoretical, 1, 11, 15, 26, 28, 29, 55, 66, 91 theory, 6, 7, 8, 11, 14, 15, 24, 26, 28, 29, 35, 36, 40, 41, 42, 45, 49, 50, 51, 52, 53, 54, 55, 66, 92 thermodynamics, 16 thinking, 2 threshold, 3, 4 time, vii, 2, 22, 23, 24, 25, 26, 30, 31, 33, 39, 41, 43, 44, 47, 48, 50, 51, 52, 57, 62, 66, 69, 70, 71, 75, 76, 86, 87, 90, 91, 92, 93, 94, 95 time consuming, 23 timing, 33 trade, 3, 4 tradition, 29 trainees, 60, 72, 73, 75, 79, 82 training, 5, 6, 13, 17, 26, 27, 29, 30, 49, 52, 54, 59, 72, 73, 75, 76, 78, 79, 82, 84, 85, 92, 95 transfer, 17, 34, 43, 44, 45, 48, 56, 72, 85, 92 transfer performance, 44 transformation, 44 transformations, 1, 3 transition, 19, 26, 31, 34, 91 troubleshooting, 21, 29, 30, 72 tutoring, 30 two-dimensional, 71

U university students, 70

V values, 8, 13, 42, 43, 72 variable, 8 variables, 8, 15, 44

Index variation, 29 variety of domains, 21, 91 verbalizations, 56 visual, 3, 8, 41, 49, 52, 53, 54, 55, 56, 57, 58, 60, 70, 72, 73, 92, 94, 95 visual images, 3 visual stimuli, 56 visual stimulus, 52 visuospatial, 3, 53 vocabulary, 69

W water, 52

107

windows, 2, 95 witnesses, 12 word processing, 50 words, 3, 5, 6, 7, 12, 24, 36, 38, 52, 72 work, 18, 25, 27, 28, 56, 60 working memory, 2, 3, 4, 5, 6, 7, 8, 11, 13, 15, 17, 18, 22, 23, 24, 25, 30, 31, 32, 33, 34, 35, 36, 37, 39, 40, 41, 44, 48, 52, 53, 54, 55, 56, 57, 60, 61, 62, 63, 64, 66, 70, 71, 72, 74, 79, 83, 86, 87, 90, 91, 92, 93 writing, 76, 78, 79, 80