Abstract
In this chapter we discuss two important research topics surrounding student modelling: 1) how to represent knowledge about a student at various grain sizes and reason with this knowledge to enhance the capabilities of an intelligent tutoring system, and 2) how to maintain a consistent view of a student’s knowledge as the system-student interaction evolves. The ability to represent and reason about knowledge at various levels of detail is important for robust tutoring. A tutor can benefit from incorporating an explicit notion of granularity into its representation and can take advantage of granularity-based representations in reasoning about student behaviour. As the student’s understanding of concepts evolves and changes, the student model must track these changes. This leads to a difficult student model maintenance problem. Both of these topics are full of interesting subtleties and deep issues requiring years of research to be resolved (if they ever are), but a start has been made. In this chapter we characterize the main requirements for each topic, discuss some of our work that tackles these topics, and, finally, indicate important areas for future research.
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References
Anderson, J. & Reiser, B.: The LISP tutor, Byte, 10 (4), pp. 159–175 (1985)
Bhuiyan, S. H., Greer, J. E. & McCalla, G. I.: Characterizing, rationalizing, and reifying mental models of recursion. Proceedings of the 13th Annual Meeting of the Cognitive Science Society, Chicago, IL, pp. 120–125, Hillsdale, NJ: Lawrence Erlbaum Associates 1991
Brecht (Wasson), B.: Determining the Focus of Instruction: Content Planning for Intelligent Tutoring Systems, Ph.D. Thesis, Department of Computational Science, University of Saskatchewan 1990
Brecht (Wasson), B., McCalla, G. I., Greer, J. E. & Jones, M.: Planning the content of instruction. In: Proceedings of the 4th International Conference on Artificial Intelligence and Education, Amsterdam (D. Bierman, J. Breuker & J. Sandberg, eds.)., pp. 32–41. Amsterdam: IOS 1989
Brown, J. & Burton, R.: Diagnostic models for procedural bugs in basic mathematical skills, Cognitive Science Journal, 2, pp. 155–191 (1978)
Brown, J. & Van Lehn, K.: Repair theory: a generative theory of bugs in procedural skills, Cognitive Science Journal, 4, pp. 379–426 (1980)
Cohen, P. R. & Perrault, C. R.: Elements of a plan-based theory of speech acts, Cognitive Science, 3, pp. 177–212 (1989)
Corbett, A. & Anderson, J.: Feedback timing and student control in the LISP Intelligent Tutoring System. In: Proceedings of the 4th International Conference on Artificial Intelligence and Education, Amsterdam (D. Bierman, J. Breuker & J. Sandberg, eds.)., pp. 64–72. Amsterdam: IOS 1989
de Kleer, J.: An assumption-based truth maintenance system, Artificial Intelligence Journal, 28, pp. 127–162 (1986)
Doyle, J.: A truth maintenance system, Artificial Intelligence Journal, 12, pp. 231–272 (1979)
Elsom-Cook, M.: Guided Discovery Tutoring: a Framework for ICAI Research. London: Paul Chapman 1989
Goldstein, I.: The genetic graph: a representation for the evolution of procedural knowledge. In: Intelligent Tutoring Systems (D. Sleeman & J. S. Brown, eds.).pp. 51–78, London: Academic Press 1982
Greer, J. E. & McCalla, G. I.: A computational framework for granularity and its application to educational diagnosis. Proceedings of the 11th International Joint Conference on Artificial Intelligence, Detroit, MI pp. 477–482 1989
Greer, J. E., Mark, M. & McCalla, G. I.: Incorporating granularity-based recognition into SCENT. In: Proceedings of the 4th International Conference on Artificial Intelligence and Education, Amsterdam (D. Bierman, J. Breuker & J. Sandberg, eds.)., pp. 107–115. Amsterdam: IOS 1989
Hobbs, J.: Granularity. Proceedings of the 9th International Conference on Artificial Intelligence, Los Angeles, CA, pp. 432–435 1985
Huang, X., McCalla, G. I. & Greer, J. E.: Student model revision: evolution and revolution. Proceedings of the 8th Canadian Conference on Artificial Intelligence, Ottawa, pp. 98–105 1990
Huang, X.: Updating Belief Systems: Minimal Change, Focus of Attention and Stereotypes. Ph.D. Thesis, Department of Computational Science, University of Saskatchewan 1991
Huang, X., McCalla, G. I., Greer, J. E. & Neufeld, E.: Revising deductive knowledge and sterotypical knowledge in a student model, User Modelling and User Adapted Interaction, 1 (1), pp. 87–115 (1991)
Huang, X., McCalla, G. I.. & Neufeld, E.: Using attention in belief revision. Proceedings of the American Association for Artificial Intelligence Conference, Anaheim, CA, pp. 275–280 1991
Huang, X. & McCalla, G. I.: Instructional planning using focus of attention. In: Proceedings of the 2nd International Conference on Intelligent Tutoring Systems, Montreal, Quebec (C. Frasson, G. Gauthier & G. McCalla, eds.), pp. 433–450, Lecture Notes in Computer Science, Vol. 608, Berlin: Springer-Verlag 1992
Huang (1993). Modelling a student’s inconsistent beliefs and attention. In: Student Modelling: The Key to Individualized Knowledge-Based Instruction ( G. I. McCalla & J.E. Greer, eds.), pp. 277–290, Berlin, FDR: Springer-Verlag 1993
Macmillan, S. & Sleeman, D.: An architecture for a self-improving instructional planner for intelligent tutoring systems, Computational Intelligence Journal, 3, pp. 17–27 (1987)
McCalla, G. I.: Knowledge Representation Issues in Automated Tutoring, Research Report 87–1, ARIES Laboratory, Department of Computational Science, University of Saskatchewan 1987
McCalla, G. I., Greer, J. E. & the SCENT Research Team: SCENT-3: an architecture for intelligent advising in problem solving domains. In: Intelligent Tutoring Systems: At The Crossroads of Artificial Intelligence and Education (C. Frasson & G. Gauthier, eds.), pp 140–161, Norwood, NJ: Ablex 1989
McCalla, G. I. & Greer, J. E.: Helping novices learn recursion: giving granularity-based advice on strategies and providing support at the mental model level. Proceedings of the NATO Advanced Research Workshop on Cognitive Models and Intelligent Environments for Learning Programming, Genoa, Italy, pp. 57–71 1992
McCalla, G. I., Greer, J. E., Barrie, B. & Pospisil, P.: Granularity hierarchies, International Journal of Computers and Mathematics with Applications (Special Issue on Semantic Networks), 23, pp. 363–375 (1992)
Peachey, D. & McCalla, G. I.: Using planning techniques in intelligent tutoring systems, International Journal of Man-Machine Studies, 24, pp. 77–88 (1986)
Reiser, B., Anderson, J. & Farrell, R.: Dynamic student modelling in an intelligent tutor for LISP programming. Proceedings of the 9th International Conference on Artificial Intelligence, Los Angeles, CA, pp. 8–14, 1985
Reiter, R.: Diagnosis from First Principles, Artificial Intelligence, 32, pp. 57–95 (1987)
Spensley, F. & Elsom-Cook, M.: Dominie: Teaching and Assessment Strategies, CITE Report 37, Centre for Information Technology in Education, Institute of Educational Technology, The Open University, Milton Keynes, UK 1988
Weber, G.: Analogies in an intelligent programming environment for learning LISP. In: Cognitive Models and Intelligent Environments for Learning Programming (E. Lemut, B. du Boulay, G. Deltori, eds.), pp. 210–219, NATO ASI Series F, Vol. 111, Berlin: Springer-Verlag 1993
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McCalla, G.I., Greer, J.E. (1994). Granularity-Based Reasoning and Belief Revision in Student Models. In: Greer, J.E., McCalla, G.I. (eds) Student Modelling: The Key to Individualized Knowledge-Based Instruction. NATO ASI Series, vol 125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03037-0_2
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DOI: https://doi.org/10.1007/978-3-662-03037-0_2
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