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Problem Solving and Explanation in Intelligent Tutoring Systems: Issues for Future Research

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New Directions in Educational Technology

Part of the book series: NATO ASI Series ((NATO ASI F,volume 96))

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Abstract

The key to building effective intelligent tutoring systems is the representation of knowledge in the tutor’s problem solver. The problem solving knowledge determines the reasoning of students that the system can understand and the type of feedback the system can provide. We discuss the form of problem solving knowledge in intelligent tutors and the use of problem solving knowledge to provide guidance and feedback. We argue that model tracing tutors can be extended by building more underlying knowledge into their rule bases, and briefly describe GIL, a programming tutor built upon this elaborated model. We describe some of the current issues facing intelligent tutoring research, including the integration of rule-based and qualitative reasoning, timing and content of feedback, pedagogical strategies, and human- computer interface issues.

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Authors and Affiliations

  1. Cognitive Science Laboratory, Princeton University, Princeton, NJ, 08542, USA

    Brian J. Reiser

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  1. Brian J. Reiser
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Editors and Affiliations

  1. Institute of Educational Technology, The Open University, Walton Hall, MK7 6AA, Milton Keynes, UK

    Eileen Scanlon  & Tim O’Shea  & 

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© 1992 Springer-Verlag Berlin Heidelberg

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Reiser, B.J. (1992). Problem Solving and Explanation in Intelligent Tutoring Systems: Issues for Future Research. In: Scanlon, E., O’Shea, T. (eds) New Directions in Educational Technology. NATO ASI Series, vol 96. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77750-9_17

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  • DOI: https://doi.org/10.1007/978-3-642-77750-9_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-77752-3

  • Online ISBN: 978-3-642-77750-9

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