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Towards new learning strategies in intelligent tutoring systems

  • Esma Aïmeur
  • Claude Frasson
  • Carmen Alexe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 991)

Abstract

Co-operative tutoring systems replace the prescriptive approach developed by traditional intelligent tutoring systems with a constructive one based on the use of the computer as a way to exchange, control and build knowledge. This paper proposes two new learning strategies, learning by disturbing and learning by co-teaching, that extend the spectrum of possibilities in terms of co-operation and place the learner into a higher degree of abstraction. Learning by disturbing method allows to check the ability of the learner to distinguish between wrong and correct solutions. Learning by co-teaching provides an example of discussions between the teacher and the co-teacher that is useful for inducing correct solutions presented in a pedagogical form. Co-operation can be improved using elicitation techniques that can serve to extract learner's knowledge which can be further compared with the expert solution in order to identify his knowledge level. These techniques strengthen the efficiency of the learning strategies and serve as a basis for developing tutoring systems or learning environments including their co-operative aspects. We show how these strategies can be dynamically selected in an architecture of an intelligent tutoring system in which the knowledge level of the learner is frequently evaluated. We give an example of eliciting dialogues in a medical environment.

keywords

Intelligent tutoring systems Elicitation Troublemaker Co-teacher Learning strategy Co-operative system 

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References

  1. Aïmeur, E. & Frasson, C. (1995a). Application d'une méthode d'explicitation dans un environnement de système tutoriel coopératif. Environnements interactifs d'apprentissage avec ordinateur, tome 2, Editions Eyrolles, (pp. 197–208).Google Scholar
  2. Aïmeur, E. et Frasson C. (1995b) Eliciting The Learning Context In Co-Operative Tutoring Systems, IJCAI-95 Workshop on Modelling Context in Knowledge Representation and Reasoning, Montréal.Google Scholar
  3. Boose, J. & Bradshaw, J.M. (1993). Expertise Transfer and Complex Problems: using AQUINAS as a Knowledge-Acquisition Workbench for Knowledge-Based Systems. In Readings in Knowledge Acquisition and Learning, (pp. 240–252), Edited by B.G. Buchanan & D.C. Wilkins, Morgan Kaufmann Publishers.Google Scholar
  4. Boy, G., Faller, B. & Sallantin, J. (1988). Acquisition et ratification des connaissances. In Actes des journées nationales du PRC-GRECO, Intelligence Artificielle, (pp. 321–356), Toulouse.Google Scholar
  5. Chan, T.W. & Baskin, A.B. (1990). Learning Companion Systems. In C. Frasson & G. Gauthier (Eds.) Intelligent Tutoring Systems: At the Crossroads of Artificial Intelligence and Education, Chapter 1, New Jersey: Ablex Publishing Corporation.Google Scholar
  6. Chi, M.T.H., Bassok, M., Lewis, M.W., Reimann, P. & Glaser, R. (1989). Self Explanations: How students study and use examples in learning to solve problems. Cognitive Science, 5, (pp. 121–152).Google Scholar
  7. Firlej, M. & Hellens, D. (1991). Knowledge Elicitation: a Practical Book Prentice Hall International (UK) Ltd.Google Scholar
  8. Frasson, C., Kaltenbach, M. (1993). Strengthening the Novice-Expert shift using the self-explanation effect. Journal of Artificial Intelligence in Education, special issue on student modelling, vol 3(4), (pp. 477–494).Google Scholar
  9. Gagné R.M., (1984). The conditions of learning, 4 ed, Les éditions HRW Ltée, Montréal.Google Scholar
  10. Gaines, B. R. & Shaw, M. L. (1992). Knowledge acquisition tools based on personal construct psychology. Knowledge Engineering Review, 8, (pp. 49–85).Google Scholar
  11. Gallouïn, J.F. (1988).Transfert de Connaissances, Systèmes Experts: Techniques et Méthodes, Editions Eyrolles.Google Scholar
  12. Gilmore, D. & Self, J. (1988). The application of machine learning to intelligent tutoring systems. In J. Self, (Ed.) Artificial Intelligence and Human Learning, Intelligent computer-assisted instruction, New York: Chapman and Hall, (pp. 179–196).Google Scholar
  13. Major, N.P. (1991). CATO An Automated Card Sort Tool. In Proceedings of the Fifth European Knowledge Acquisition for Knowledge-Based Systems Workshop, University of Strathclyde.Google Scholar
  14. Palthepu, S., Greer, J., & McCalla, G. (1991). Learning by Teaching. The Proceedings of the International Conference on the Learning Sciences, AACE.Google Scholar
  15. Rugg, G., Mcgeorge, P. & Shadbolt, N. (1990) On the use of laddered grids in knowledge elicitation. Artificial Intelligence Group, Technical Report, Feb, 1, University of Nottingham.Google Scholar
  16. Self, J. (1988). Bypassing the untractable problem of student modelling. International Conference of Intelligent Tutoring Systems, Montreal, Canada, (pp. 18–24).Google Scholar
  17. Van Lehn, K, Ohlsson, S. & Nason, R. (1994). Application of simulated students: an exploration. Journal of artificial intelligence in education, vol 5, no 2, (pp.135–175).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Esma Aïmeur
    • 1
  • Claude Frasson
    • 1
  • Carmen Alexe
    • 1
  1. 1.Département d'informatique et de recherche opérationnelleUniversité de MontréalMontréalCanada

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