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Explanatory Mechanisms for Intelligent Tutoring Systems

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 1452)

Abstract

QUE is an exploratory environment for users of rule based intelligent systems. Its original motivation was the question of how to analyze and explain the discrepancies in rule-based intelligent tutoring systems, between “near miss” incorrect responses of a student and the system’s knowledge of the “correct” line of reasoning. It is currently under development as a suite of techniques which provide explanation by supporting the exploration of a system’s reasoning processes. This paper describes some of the exploratory modes, the underlying mechanisms that support them, and a number of ways in which these modes and mechanisms might be incorporated into intelligent tutoring architectures.

Keywords

Explanatory Mechanism Reasoning Process Intelligent Tutor System Exploratory Environment Exploratory Mode 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    Domingue, J.; TRI: The transparent rule interpreter, Research and Development in Expert Systems V, B. Kelly and A. L. Rector (Eds.), Cambridge Univ. Press, 126–138, 1988.Google Scholar
  2. 2]
    Wickler, G., Chappel, H. & Lambert, S.; An architecture for a generic explanation component, Presented at IJCAI Workshop on Explanation and Problem Solving, 1993.Google Scholar
  3. [3]
    Slotnick, S. A. & Moore, J. D.; Explaining quantitative systems to uninitiated users, Expert Systems with Applications, 8(4), 475–490, 1995.CrossRefGoogle Scholar
  4. [4]
    Wick, M. R., Pradyumna, D., Wineinger, T. & Conner, J.; Reconstructive explanation: a case study in integral calculus, Expert Systems with Applications, 8(4), 463–473, 1995.CrossRefGoogle Scholar
  5. [5]
    Maybury, M. T.; Communicative acts for explanation generation, International Journal of Man-Machine Studies, 37( 2), 135–172, 1992.CrossRefGoogle Scholar
  6. [6]
    Cawsey, A.; Explanation and Interaction: The Computer Generation of Explanatory Dialogue, MIT Press, 1992.Google Scholar
  7. [7]
    Suthers, D. D.; Answering students queries: functionality and mechanisms, Intelligent Tutoring Systems: Proceedings of the Second International Conference, C. Frasson, G. Gauthier & G. I. McCalla (Eds.), Montreal, Canada, Springer-Verlag. 191–198, 1992.Google Scholar
  8. [8]
    Metzler, D. P., & Martincic, C. J.; Explanation of negative and hypothetical questions in a rule-based reasoning system. Proc. of 5th. International Symposium on Systems Research, Informatics and Cybernetics, Baden-Baden Germany, 1995.Google Scholar
  9. [9]
    Michalski, R. S.; A theory and methodology of inductive learning, Artificial Intelligence, 20, 111–161, 1983.CrossRefMathSciNetGoogle Scholar
  10. [10]
    Lenat, D. & Guha, R. V.; Building Large Knowledge-Based System, Addison-Wesley, 1989.Google Scholar
  11. [11]
    Rosch, E., Mervis, C.B., Gray, W. D., Johnson, D. M. & Boyes-Braem, P.; Basic objects in natural categories, Cog. Psychology, 8, 382–439, 1976.CrossRefGoogle Scholar
  12. [12]
    Sypniewski, B. P.; The importance of being data, AI Expert, 9(11), 23–31, 1994.Google Scholar
  13. [13]
    Chi, M. T. H., de Leeuw, N. Chiu, M. H. & LaVancher, C. Eliciting self-explanation improves understanding. Cog. Science, 8, 439–477, 1994.CrossRefGoogle Scholar
  14. [14]
    Katz, S., Schmandt, L. & Metzler, D.; A Prototype Tutoring System for Subject Cataloging. Department of Information Science, University of Pittsburgh, Tech. Rept. IS89005, 1989Google Scholar
  15. [15]
    Self, J. A. Bypassing the intractable problem of student modeling. Intelligent Tutoring Systems: At the Crossroads of Artificial Intelligence and Education. Frasson, C. and Gauthier, G. (Eds.), Norwoord, N. J.: Ablex Publishing, 110–123, 1990.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  1. 1.Dept. of Information Science & TelecommunicationsUniversity of PittsburghPittsburghUSA

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