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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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