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Using expert tutor knowledge to design a Self-Improving intelligent tutoring system

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Intelligent Tutoring Systems (ITS 1992)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 608))

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Abstract

What knowledge does an intelligent tutoring system need to learn from its experiences with students and improve its tutoring, and what are the necessary learning mechanisms? I address these in discussing (1) SIFT, a Self-Improving Fractions Tutor and (2) my study of an expert tutor on whose knowledge SIFT is based. SIFT is a production system with a tutor and a learning module which learns from its interactions with the students who use it. The students who use it are models of problem solvers, and the input transcripts are simulations of interactions. After augmenting its knowledge, SIFT evaluates its modifications and updates its rule probabilities using the Dempster-Shafer theory of evidence—a domain-independent modification. Thus its choice of which rule to fire is determined by the empirical effects of the changes it makes to its tutorial knowledge.

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Authors and Affiliations

  1. Computer Science Department, University of Wisconsin-Madison, 53706, Madison, WI

    Eric Gutstein

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  1. Eric Gutstein
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Editor information

Claude Frasson Gilles Gauthier Gordon I. McCalla

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© 1992 Springer-Verlag Berlin Heidelberg

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Gutstein, E. (1992). Using expert tutor knowledge to design a Self-Improving intelligent tutoring system. In: Frasson, C., Gauthier, G., McCalla, G.I. (eds) Intelligent Tutoring Systems. ITS 1992. Lecture Notes in Computer Science, vol 608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55606-0_72

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  • DOI: https://doi.org/10.1007/3-540-55606-0_72

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-55606-0

  • Online ISBN: 978-3-540-47254-4

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