Abstract
In this paper we present an approach to teach classification knowledge. Our system TUDIS allows students to perform the complete process of solving a classification problem with the hypothesize-and-test strategy, that means interpreting the initially given data to data abstractions, generating and evaluating hypotheses and selecting additional tests to valuate the hypotheses. The student's actions are compared to those of the underlying expert system and the correspondences and relevant discrepancies are presented to the student. These results may be explained further by invocation of the explanation component of the expert system or the hypertext component. TUDIS should be used in combination with a standard textbook introducing the domain to be learned, and a knowledge base representing the knowledge of the book. Students can study the book as usual and examine their understanding of the domain by use of TUDIS. To bridge the gap between the informal textbook and the formal knowledge base, we use a hypertext component, which allows to represent informal conventional representations such as text, pictures and tables in a computer and to link these between each other and also to objects of the knowledge base.
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Poeck, K., Tins, M. (1993). An intelligent tutoring system for classification problem solving. In: Jürgen Ohlbach, H. (eds) GWAI-92: Advances in Artificial Intelligence. Lecture Notes in Computer Science, vol 671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0019006
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DOI: https://doi.org/10.1007/BFb0019006
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