Abstract:
Abstract This chapter presents how Machine Learning Techniques can effectively contribute to improve the quality of interactions in Guided Discovery Tutoring Environments (GDTE). We review several approaches to integrate Machine Learning in ITS. Most of these approaches use concept learning from examples to maintain a Student Model. We go along presenting an alternative use of induction techniques to learn concepts on the same data that are presented to the learner. We present on a concrete example how this approach is integrated in a GDTE called MEMOCAR, a Computer Aided Language Learning System for Chinese characters. Three main types of activity are identified in MEMOCAR: familiarization with Chinese characters, collaborative discovery of similarities between characters and exercises to test characters acquisition. The stage of familiarization is supported by exploration of hyperdata whilst collaborative discovery and exercises ' diagnosis are supported by a tool based on CHARADE, a top-down induction system. Such integration offers a new alternative to the complex problem of making Guided Discovery Tutoring Environment more collaborative. Rsum: Le thme abord dans ce rapport est celui de l'utilisation de techniques
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