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Expert-Novice Differences and Adaptive Multimedia 217 Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. The usability of the rapid verification method as a means of real-time adaptation of multimedia (diagrams with on-screen textual explanations) presentations to current levels of users expertise in solving vector addition motion problems was pilot-tested with a limited sample of 16 Grade 11 high-school students in the school s computer lab. Using an adaptive computer-based tutor, the learner-adapted procedure was compared to an equivalent instruction without real-time adaptation to the level of learner expertise. The training packages were designed using Authorware Professional and included an initial rapid diagnostic test, an adaptive training session for the experimental group and a non-adaptive version for the control group, and a final rapid diagnostic test (similar to the initial test with re-worded tasks). To limit the task domain to relatively simple classes of problems, a restricted range of angle values between vectors were used in the tasks: 0 (the same direction of movements), 90 (perpendicular vectors), 180 (opposite directions of movements), 60 , and 120 . When 60 or 120 angles are used, only equal velocity values for both vectors were allowed. The diagnostic items in this restricted domain included one diagnostic task statement for each angle value, followed by a series of five possible (both correct and incorrect) solution steps for rapid verification with gradually-increasing levels of graphical and numerical solution details provided to students. For example, the first step indicated only directions of vectors; the second step also showed their numerical values; the third step indicated the direction of the resulting vector; the fourth step included the numerical expression for calculating the value of the resulting vector; and the fifth step indicated only the final answer. Each task statement was presented to students for around 15 seconds that were sufficient for reading the statement. Instead of technically restricting response times to several seconds (what could have forcefully interrupted some genuine responses), students were coached in responding fast during pre-test exercises with a sample of tasks from a different area. Users prior knowledge is an important factor contributing to individual differences in the effect of instruction based on text and visual displays (Schnotz, 2002). For learners with lower levels of expertise, based on the rapid diagnostic assessment, additional pictorial and textual information was provided. For learners with higher levels of expertise, redundant representations were eliminated. The adaptive training session was based on a series of faded worked examples or completion tasks (Renkl & Atkinson, 2003; Van Merri nboer, 1990), each followed by a problem-solving practice. According to this approach, novices learn most effectively when instructed using fully worked out examples. As levels of learners knowledge in the domain increase, parts of worked examples could be gradually omitted, thus increasing a relative share of problem-solving practice in instruction. In the learner-adapted group, learners were allocated to appropriate stages of the instructional procedure corresponding to the performance break-down points that were determined by the outcomes of the initial rapid diagnostic test. Appropriate fully and partially worked-out examples were presented, each followed by a problem-solving exercise. Depending on the outcome of the rapid diagnostic probes during instruction, each learner was allowed to proceed to the next stage of the training session or was required to repeat the same stage and then take the rapid test again. At each subsequent stage of the training session, a lower level of instructional guidance was provided to learners by eliminating increasingly more explanations of initial procedural steps in faded

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Expert-Novice Differences and Adaptive Multimedia 215 Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. For example, when training apprentices of manufacturing companies in reading different cutting speed nomograms (Figure 1) used to determine the appropriate number of revolutions per minute to run a specific type of cutting machines (Kalyuga, et al., 2000), we observed that replacing visual texts with corresponding auditory explanations was beneficial for novice learners (modality effect). 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. However, when learners became much more experienced in using different nomograms, the best way to present them a new type of nomograms was to display just a diagram without any explanations at all (an example of the expertise reversal effect). The rapid test that may allow us to switch instructional formats at the most appropriate time for an individual trainee can be based on regularly presenting trainees partially completed procedures (with different degrees of completeness) and asking them to rapidly indicate their next step towards solution. At the lowest level of completeness, no task information is indicated on the diagram. At the next level, only some initial data in the task statement is highlighted along the axes or in the table. At the following levels, more lines and their intersection points are shown. In this way, levels of expertise can be rapidly determined. Accordingly, less expert participants, as determined by the rapid test, should be presented with comprehensive auditory explanations. In contrast, more expert participants, for whom the auditory explanations might be redundant, would perform better with a diagram and limited or no explanations. The rapid testing method was applied for real-time adaptation of instructional procedures (worked examples and problem-based instruction) to current levels of individual learners knowledge in the domain of linear algebra equations using a simple adaptive computer-based tutor (Kalyuga & Sweller, 2004). The aim of the experiment was to demonstrate that the rapid test could be effectively used in a computer-based training environment for adapting instruction to changing levels of learners knowledge of solution procedures. The rapid test was used for initial selection of the appropriate levels of instructional materials according to levels of learners preliminary knowledge, as well as for monitoring learners progress during instruction and real-time selection of the most appropriate instructional formats. For learners with lower levels of expertise, based on the rapid test, additional worked-example information was provided. For learners with higher levels of expertise, less worked-example information and more problem-solving exercises were provided. The learner-adapted procedure was compared to an equivalent procedure without realtime adaptation of instruction to levels of learner knowledge. Learning was enhanced by adapting instruction to learner levels of expertise (effect size 0.46 for relative knowledge gains due to instruction). This study provided evidence for the usability of the rapid test, although in a relatively simple and not media-rich domain. Similar rapid diagnosis-based approaches could be used in other more complex environments for initial selection of the appropriate formats of multimedia materials according to levels of users preliminary knowledge in the domain, monitoring their progress during training, and real-time selection of the most appropriate multimedia formats to build a fully learner-adapted presentation.

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216 Kalyuga Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Studying the Usability of the Adaptive Procedure Rapid diagnostic tasks in many complex domains require responses that cannot always be specified precisely in advance. For example, for many tasks in physics or engineering, drawing graphical representations is essential, and these drawings are usually very subjective. In paper-based formats, the above first-step method could still be applied in all these situations. However, when indicating a first-step response requires graphical representations, recording and analyzing students responses in computer-based multimedia environments may be technically challenging. In such situations, an alternative rapid diagnostic approach could be based on users rapid verifications of suggested solution steps. With the rapid verification method, after studying a task for a limited time, users are presented with a series of possible (both correct and incorrect) solution steps reflecting various stages of the solution procedure, and are asked to rapidly verify the suggested steps (for example, by pressing corresponding keys on the computer keyboard). For example, for the vector addition motion task A crane is moving horizontally at 3 m/s. A load is being lifted at 1 m/s. What is the velocity of the load relative to the ground?, each solution verification window may include a diagrammatic and/or numerical representation of a possible (correct or incorrect) solution step and buttons Right , Wrong , and Don t know for students to click on (see Figure 2 for an example of a suggested incorrect step). The Don t know button was included as the third answer option in order to reduce a possible guessing effect. Figure 2. A suggested incorrect solution step for a rapid verification diagnostic task

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214 Kalyuga Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Similar first-step diagnostic tasks could be used to determine optimal instructional procedures for individuals with different levels of expertise in a domain. For example, based on the expertise reversal effect, we know that presenting novice learners with worked examples is superior to presenting them with problems to solve, but that more knowledgeable learners should be presented with problems rather than worked examples (Kalyuga, Chandler, Tuovinen, & Sweller, 2001). We do not know at what point the switch from examples to problems should occur because we have not had a suitable diagnostic instrument to provide us with levels of expertise. The suggested diagnostic technique is just such an instrument. In a preliminary experiment using coordinate geometry, we were able to use the rapid test to successfully predict which students should be presented with worked examples and which should be presented with problems (Kalyuga & Sweller, 2004). Similarly, the rapid diagnostic test can be used to predict whether learners should be presented with information in integrated or dual-modality format (novices) or in non-redundant diagrammatic format (more expert learners). In other words, the rapid test can determine the point at which information should no longer be presented in integrated format (e.g., textual explanations embedded into a diagram or presented as auditory narrations) but rather be presented as a single diagram without any textual (on-screen or auditory) explanations. Figure 1. A snapshot of the multimedia instructional format for the cutting speed nomogram (adapted from Kalyuga, Chandler, & Sweller (2000); copyright 2000 by the American Psychological Association, Inc)

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Expert-Novice Differences and Adaptive Multimedia 213 Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Overall, it is possible to suggest that multimedia presentations could be more cognitively efficient if they continuously and dynamically tailor formats of information presentation to changing levels of user proficiency in a domain. Appropriately-constructed rapid diagnostic methods based on immediate traces of the content of long-term working memory while users approach a task or solve a problem, could be used to dynamically monitor levels of expertise in the domain. Rapid cognitive diagnosis in combination with principles for optimizing cognitive load derived from the expertise reversal effect could provide effective adaptive procedures. Developing appropriate rapid cognitive diagnostic techniques is the key task in implementing this adaptive multimedia methodology. As mentioned previously, long-term memory structures define the characteristics of our performance during knowledgebased cognitive activities. If a person is facing a task in a familiar domain, and her or his immediate approach to this task is based on available knowledge structures, these structures will be rapidly activated and brought into the person s working memory. A corresponding LTWM structure will be created. These LTWM structures are durable and interference-proof to allow sufficient time for a practically-usable diagnostic procedure. There is no need to capture the immediate content of memory strictly within a split-second of corresponding cognitive operations. The available time could be sufficient for recording or otherwise registering task responses in a suitable format. The general idea of this diagnostic approach is to determine the highest level of organized knowledge structures (if any) that a person is capable of retrieving and applying rapidly to a task or situation she or he encounters. The approach has been realized as the first-step diagnostic method. Learners were presented with a task for a limited time and asked to indicate their first step towards solution (Kalyuga & Sweller, 2004). The first step would involve different cognitive operations for individuals with different levels of expertise in a domain. An expert may immediately provide the final answer; a less knowledgeable person may indicate the very first operation according to a detailed step-by-step solution procedure; and a novice may start some search process, for example, using a trial-and-error technique. Therefore, different first-step responses would reflect different levels of acquisition of corresponding knowledge structures. Skipping some intermediate procedural operations when showing the first subjectively-significant solution step would indicate a higher level of proficiency: the expert may have corresponding operations automated or well-learned to be able to perform them mentally without exceeding working memory capacity. The first-step method was used (both in paper- and computer-based formats) to diagnose secondary and high-school students knowledge of procedures for solving linear algebraic equations, simple coordinate geometry tasks, and arithmetic word problems (Kalyuga & Sweller, 2004; Kalyuga, in press), and for testing reading comprehension skills (Kalyuga, 2006) Experimental results indicated significant correlations (.72 - .92) between performance on these tasks and traditional measures of knowledge that required complete solutions of corresponding tasks. Test times were reduced by factors of up to 4.9 in comparison with traditional test times. The first-step diagnostic method was not only less time-consuming but also more sensitive to underlying knowledge structures than traditional tests.

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212 Kalyuga Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. data available from tracing user interactions with the system are usually imprecise, incomplete, and uncertain. Applying modern artificial intelligence approaches and methods (e.g., machine learning, Bayesian inference networks, neural networks, etc.) could help increase the precision of adaptive technologies. For example, intelligent solution analyses could diagnose missing or defective components of knowledge and skill, and provide learners with more accurate feedback and support. On the other hand, quality of adaptive environments could also be improved by developing new cognitive diagnostic techniques to replace traditional assessment methods used in constructing user models. The following sections describe a possible implementation of this approach. Rapid Diagnostic Method for Tailoring Multimedia to Levels of User Expertise The research on expertise emphasizes the importance of diagnosing domain-specific organized knowledge structures when evaluating levels of proficiency. Traditional methods of knowledge assessment are usually lengthy and limited in their ability to rapidly diagnose different levels of knowledge acquisition. They are not suitable for realtime, on-line adaptation of multimedia formats to dynamically changing levels of expertise. Available methods of cognitive diagnosis used in cognitive laboratory studies (e.g., concurrent and retrospective reporting, observations, etc.) are also unfit for realtime monitoring of user performance in adaptive digital multimedia environments because they are very time-consuming. Therefore, no appropriate, cognitively-oriented diagnostic methods are available to be used in adaptive procedures for user-tailored multimedia environments. The content of users knowledge base could not be accessed directly. Usually, we are able to obtain some evidence of that knowledge from results of various cognitive activities (e.g., solving test problem) and make probabilistic inferences about possible underlying cognitive constructs. This evidence could be inadequate in many situations. For example, students answers to a series of test problems would not tell us if those problems were solved by using a novice-like search approaches or an expert-like method based on knowledge of appropriate solution procedures (or, in the latter case, what level of knowledge was applied). We could do better in cognitive diagnosis if we were able to rapidly register immediate traces of individuals use of their knowledge structures while they approach a problem or situation. The diagnostic power of this method could approach that of concurrent reporting or think-aloud diagnostic techniques; however, it could work on a considerably shorter time scale. The rapid tracing of currentlyactivated knowledge structures essentially means accessing and monitoring content of working memory or, more accurately LTWM, since we are diagnosing knowledge-based cognitive performance. Therefore, to evaluate user levels of expertise in real-time, we may need to rapidly diagnose the content of LTWM during complex cognitive activities. With this approach, LTWM characteristics are used to determine relevant components of knowledge base held in long-term memory.

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210 Kalyuga Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. hand, recently established cognitive load effects in multimedia design for more advanced learners suggest eliminating non-essential redundant representations in multimedia formats and gradually reducing levels of guidance by increasing the relative share of problem-based and exploratory environments as levels of user proficiency in the domain increase (Kalyuga, 2005). Thus, studies of expert-novice differences have demonstrated that organized schemabased knowledge structures in long-term memory are the most critical factor influencing proficient performance. These cognitive constructs effectively reduce or eliminate severe processing limitations of our cognitive system and fundamentally alter characteristics of our performance. They guide allocation of attentional resources and significantly influence our perception of multimedia materials. Non-optimal multimedia formats may overload limited attentional capacity of working memory. As a consequence, multimedia presentations which include information that is essential and appropriate for novices, may need to be re-designed by eliminating redundant information for more expert individuals in order to optimize cognitive resources. An important implication of these findings is that multimedia needs to be tailored to levels of user expertise in a domain. To be able to dynamically select multimedia formats optimal for individual users, it is necessary not only to understand cognitive mechanisms that influence efficiency of multimedia information presentations, but also to have suitable methods for collecting information about user levels of proficiency in a domain suitable for real-time applications. User Modelling in Adaptive Hypermedia Environments Hypermedia systems add navigation support to traditional linear multimedia environments. This capability provides appropriate levels of user interactivity and user control implemented as an organized network of hyperlinks that allow nonlinear access to graphics, sound, animation, and other multimedia elements. Adaptive hypermedia environments accommodate user characteristics (knowledge, interests, goals, etc.) into an explicit user model and then use this model to adapt interactions with each user to her or his characteristics and needs, for example, by providing adaptive content selection and presentation, or suggesting a set of most relevant links to proceed (see Brusilovsky, 2001; De Bra & Calvi, 1998; Kobsa, 2001, for comprehensive overviews of the field). Adapting the content modality to an individual user (selecting the most relevant modes of presentation from text, narration, animation, video, etc.) is an important part of adaptive presentation techniques based on the user-modeling technology. User models (student or learner models in learning systems) represent the key component of an adaptive hypermedia system. These models are multi-dimensional constructs that may include many different user characteristics in addition to subject matter knowledge, for example, level of computer literacy, experience in using specific software applications, learning styles, background, preferences, goals, interests, and so forth. User models are

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Expert-Novice Differences and Adaptive Multimedia 211 Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. usually constructed by using traditional testing and survey methods, or recording the history of user interactions with the system (e.g., browsing behaviour or navigation trace) to determine users knowledge and experience, background, interests, preferences, learning styles, and other characteristics. These models are regularly updated as users work their way through the environment. User models are utilized by the system to individualize components of the content and user activities (the domain model) according to a specified adaptive methodology (the adaptation model). For example, AHA (Adaptive Hypermedia Architecture) system (De Bra & Calvi, 1998) includes an engine that maintains a user model based on knowledge of the concepts involved in a domain. The model is generated as the user reads pages and takes tests. Depending on the user s knowledge, different fragments of the learning material are presented. The user is guided towards more appropriate pages that contain information most relevant at that time by hiding, removing, or disabling less appropriate links, or by providing adaptive link annotations (e.g., by using a specific color scheme for desired, undesired, neutral, or external links). ELM-ART and InterBook adaptive hypermedia learning environments (Brusilovsky, Eklund, & Schwarz, 1998) use history-based, knowledge-based, and prerequisite-based adaptive annotations of links to suggest a best path through a learning space. Adaptive navigation support adjusts the links accessible to a particular learner using such techniques as direct guidance, adaptive link sorting, adaptive link hiding, removal, or disabling. Adaptive tables of contents, known and required concepts, adaptive content pages, and adaptive messages about the educational status of a page (e.g., warning that the page is not yet ready to be learned) are provided. Levels of user domain expertise are usually represented by the knowledge component of traditional user models. Because domain-specific knowledge is a major factor that directly influences learning processes, it is usually included in most student models. However, the way it is modeled and the levels of granularity of the models vary considerably. In most cases, they are rather coarse-grained representations using a few numeric or categorical values (e.g., high, intermediate, low levels, or no knowledge; or just Booleans yes or no) for a few concepts. Even systems that allow many values (e.g., percentage values from 0 to 100) use only a few discrete levels in the actual adaptation process (De Bra & Calvi, 1998). Initial information about user knowledge is usually obtained from tests at the beginning of the first session or is set as default values. Thereafter, the system updates the level of knowledge in the user model based on direct assessment tests or history of student actions (e.g., number of reattempts during task solutions, number of requests for help, etc.). The adaptation model then uses the updated knowledge levels to adjust multimedia presentations for individual users. The accuracy of information in user models is one of the defining factors that influence quality of adaptive environments. An important direction of improvement of user models for adaptive hypermedia (and multimedia) environments is constructing richer and more diagnostically-informative models that capture the nature and levels of user proficiency more precisely. Using traditional (mostly multiple-choice) tests and tracing sequences of mouse clicks provide rather limited sources of diagnostic information. Analyses of student solutions to presented problems usually deal with final answers to those problems without considering details of how those answers were actually obtained. The

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Expert-Novice Differences and Adaptive Multimedia 209 Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. in long-term memory highly automated due to extensive practice. To emphasize the decisive role of long-term memory in expert performance, Ericsson and Kintsch (1995) proposed the theory of long-term working memory (LTWM). According to this theory, long-term memory structures associated with components of working memory create a LTWM structure that is capable of holding virtually unlimited amounts of information. Recent studies of the expertise reversal effect (see Kalyuga, 2005; Kalyuga, Ayres, Chandler, & Sweller, 2003, for an overview) have demonstrated that information or a learning procedure that is beneficial for novice learners may become redundant for more knowledgeable learners. The expertise reversal effect can be related to research on aptitude-treatment interactions (e.g., Cronbach & Snow, 1977; Shute, 1992) that occur when different instructional treatments result in different learning outcomes depending on student aptitudes (knowledge, skills, learning styles, personality characteristics, etc.). In the expertise reversal effect, prior knowledge is the aptitude of interest. The effect can be explained by assuming that for more knowledgeable learners, the redundant material or instructional guidance overloads working memory relative to information without redundancy because resources are required for cross-referencing presented and previously-learned information. Accordingly, cognitive efficiency of multimedia presentations is relative to levels of user proficiency in a domain. Using appropriate procedures and removing redundant information at each level of user expertise, thus minimizing interfering cognitive load, is necessary for optimizing cognitive resources when designing multimedia presentations. For example, in a set of studies conducted with technical apprentices of a manufacturing company (Kalyuga, Chandler, & Sweller, 2000), detailed auditory explanations of procedures for using specific types of diagrammatic representations (cutting speed nomograms) that were presented simultaneously with animated diagrams were cognitively optimal multimedia instructional formats for novice trainees. However, at higher levels of expertise achieved after a series of intensive training sessions, when cognitive activities of the same users were based on well-learned schematic procedures, presenting a slightly different type of nomograms with detailed auditory explanations was suboptimal. Explanations designed to support construction of schematic knowledge structures that had already been acquired by trainees were redundant and inefficient. According to cognitive theories of multimedia learning (Mayer, 2001, 2005; Sweller, 1999), when text and pictures are not synchronized in space (located separately) or time (presented after or before each other), the integration process may increase cognitive load due to cross-referencing different representations. Physically integrating verbal and pictorial representations may eliminate this split-attention effect (Mayer & Gallini, 1990; Sweller, Chandler, Tierney, & Cooper, 1990). Therefore, a cognitively-optimal design of multimedia presentations for novice users usually requires eliminating situations when attention is split between multiple complementing information representations (e.g., on-screen text and diagrams) by embedding sections of textual explanations directly into the diagram in close proximity to relevant components of the diagram. Alternatively, dual-modality formats should be used with segments of narrated text presented simultaneously with the diagram (or relevant animation frames). Also, providing detailed instructional guidance by using plenty of fully worked-out examples at the initial stages of learning is required for novice learners (Sweller, et al., 1998). On the other

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208 Kalyuga Copyright 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. novice differences. The most important consequence of the reviewed studies is that the design of effective and cognitively-efficient multimedia environments needs to be tailored to changing levels of user expertise in a domain in order to optimize cognitive resources available for understanding multimedia messages. Empirically-established interactions between levels of user expertise and different formats of multimedia presentations are described. Also, a brief overview of recently- developed technical adaptation solutions based on user modeling in the adaptive hypermedia field is provided. Finally, the chapter discusses a possible adaptive methodology that is based on real-time monitoring of users proficiency in a domain by using rapid cognitive diagnostic methods for capturing authentic domain-specific knowledge structures involved in processing presented information. This diagnostic approach may have the potential for developing more rapid and sensitive knowledge-tracing techniques than traditional tests. It could be used to increase the accuracy of information about levels of knowledge and expertise stored in an individual user model. To illustrate the approach, the rapid diagnostic method has been applied (in a preliminary pilot study) as a means of tailoring instructions to levels of learner expertise in a simple adaptive computer-based tutor in kinematics. Expert-Novice Differences in Processing Multimedia Information Two major components of our cognitive architecture that are directly related to processing multimedia information are working memory and long-term memory. Working memory provides temporary storage and transformation of verbal and pictorial information that is currently in the focus of our attention (e.g., constructing and updating mental representations of a current situation or task). If too many elements of information are processed simultaneously in working memory, its capacity may become overloaded (Baddeley, 1986; Miller, 1956). Processing limitations of working memory and associated cognitive load represent a major factor influencing the effectiveness of learning (Sweller, 1999; Sweller, van Merrienboer, & Paas, 1998). Presenting related elements of information (e.g., pictures and related words) in alternative modalities (visual and auditory) may reduce cognitive load by employing two relatively independent sub-systems of working memory responsible for dealing with visual and auditory information. In order to overcome limitations of working memory and reduce associated cognitive load, organized domain-specific knowledge structures in long-term memory allow treating multiple elements of information as a single higher-level element (Chi, Glaser, & Rees, 1982). Such structures also allow experts to rapidly classify problem situations and retrieve appropriate schematic procedures for handling these situations instead of employing cognitively demanding and inefficient search-based strategies that novices usually use. For example, studies of problem solving in physics by individuals with different levels of expertise demonstrated that experts approached the problems in terms of the basic principles of physics, while novices heavily depended on surface features mentioned in each specific task (Chi, Feltovich, & Glaser, 1981). In addition, experts are also able to bypass working memory limitations by having their knowledge structures

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