By Paula J. Durlach, Alan M. Lesgold
This edited quantity offers an outline of the most recent developments in adaptive education expertise. clever tutoring has been deployed for well-defined and comparatively static academic domain names akin to algebra and geometry. even if, this adaptive method of computer-based education has but to return into wider utilization for domain names which are much less good outlined or the place student-system interactions are much less based, resembling in the course of scenario-based simulation and immersive severe video games. with a purpose to tackle how you can extend the achieve of adaptive education expertise to those domain names, top specialists within the box current their paintings in parts similar to pupil modeling, pedagogical method, wisdom overview, common language processing, and digital human brokers. numerous techniques to designing adaptive know-how are mentioned for either conventional academic settings education domain names. This e-book will attract an individual desirous about academic and coaching expertise at a qualified point, together with researchers, education structures builders, and architects.
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Extra info for Adaptive Technologies for Training and Education
Cognitive tutors: Lessons learned. Journal of the Learning Sciences, 4, 167–207. Anderson, J. , & Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Lawrence Erlbaum. , Beal, C. , Walles, R. & Woolf, B. P. (2004). Web-based intelligent adaptive educational systems multimedia tutoring for high stakes achievement tests. Intelligent Tutoring Systems, 7th International Conference, ITS 2004, Maceiò, Alagoas, Brazil, Proceedings. Lecture Notes in Computer Science 3220 (pp. 468–477). Berlin: Springer Verlag.
Adaptive methods are used to initially assign users to 13 specific classes (stereotypes), so previously unknown characteristics can be inferred on the basis of the assumption that they will share characteristics with others in the same class (Kobsa, 2006). Creating stereotypes is a common approach to user modeling, whereby a small amount of initial information is used to assume a large number of default assumptions. When more information about individuals becomes available, the default assumptions may be altered (Rich, 1979).
No learner model is maintained. This predefined path dictates which educational resources and testing materials are presented to the learner. , 2005). 2 depicts an extended version of our model. 1). Agents share (or negotiate) personal information with other agents to accomplish goals on behalf of the learner. A common LM is maintained in a learner modeling server. , long-term goals and interests). Summary of Current Adaptive Technologies This section describes adaptive technologies currently in use and relevant to the context of this chapter.