Abstract
Intelligent tutoring systems can be individualized if they are designed to take into account differences between students. The process of doing this is called student modelling. Unfortunately, student modelling is hard, and increasingly researchers are trying to avoid the need. The idea of one-on-one tutoring is taking a back seat to new ideas like collaborative learning, negotiated tutoring, guided discovery tutoring, and situated learning, as well as old ideas like discovery learning. In this paper I will argue two things:
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first, that Sie new approaches have, if anything, even more need for student modelling than does a one-on-one tutor; and
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second, that traditional idea of designing for individualized one-on-one interaction is still. I will then consider various ways of tackling the “intractable” student modelling problem, and will conclude with some optimism for the future of student modelling and one-on-one tutoring.
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References
Allen, J.F. and Perrault, C.R.: Analyzing intention in dialogues, Artificial Intelligence, 15, 3, 143–178 (1980).
Anderson, J.R.: The architecture of cognition. Cambridge, Massachusetts: Harvard University Press 1983.
Anderson, J.R., Boyle, CF., and Yost, G.: The geometry tutor. Proceedings of the Ninth International Joint Conference on Artificial Intelligence, pp. 1–7, Los Angeles, California 1985.
Anderson, J.R. and Reiser, B J.: The LISP tutor. Byte, 10, 4, 159–175 (1985).
Baril, D.: Applying qualitative reasoning to student modelling in an intelligent tutoring system, M.Sc. Thesis, Department of Computational Science, University of Saskatchewan, Saskatoon, Canada forthcoming.
Bhuiyan, S.H., Greer, J.E., and McCalla, G.I.: Mental models of recursion and their use in the SCENT programming advisor. In: Knowledge-based computer systems (S. Ramani, R. Chandrasekar, K.S.R. Anjaneyulu, eds.), pp. 135–144. Bombay, India: Narosa 1989.
Biermann, DJ., Kamsteeg, P.A., and Sandberg, J.A.C: Student models, scratchpads and simulation. In this volume.
Bloom, B.S.: Taxonomy of educational objectives, handbook I: cognitive domain. New York: David McKay 1956.
Bloom, B.S.: The 2-sigma problem: the search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, June/July, 4–16 (1984).
Bloomfield, L.: Language. New York: Holt Rinehart 1933.
Bonar, J.G. and Cunningham, R.: Bridge: tutoring the programming process. In: Intelligent tutoring systems: lessons learned (J. Psotka, L.D. Massey, S.A. Mutter, eds.), pp. 409–434, Hillsdale, New Jersey: Lawrence Erlbaum 1988.
Brecht, B.J.: Determining the focus of instruction: content planning for intelligent tutoring systems. (Ph.D. Thesis), TR 90–5, ARIES Laboratory, Department of Computational Science, U. of Saskatchewan, Saskatoon 1990.
Brown, J.S.: Process versus product: a perspective on tools for communal and informal electronic learning. Journal of Educational Computing Research, 1, 179–201 (1985).
Brown, J.S.: Toward a new epistemology for learning. In: Intelligent tutoring systems: at the crossroads of artificial intelligence and education (C Frasson and G. Gauthier, eds.), pp. 266–282, New Jersey: Ablex 1990.
Brown, J.S. and Burton, R.R.: Diagnostic models -for procedural bugs in basic mathematical skills. Cognitive Science, 2, 155–191 (1978).
Brown, J.S., Burton, R.R., and deKleer, J.: Pedagogical, natural language, and knowledge engineering techiques in SOPHIE I, II, and III. In: Intelligent tutoring systems (D.H. Sleeman and J.S. Brown, eds.). London: Academic Press 1982.
Brown, J.S. and VanLehn, K.: Repair theory: a generative theory of bugs in procedural skills. Cognitive Science, 2, 379–426 (1980).
Burton, R.R. and Brown, J.S.: An investigation of computer coaching for informal learning activities. In: Intelligent tutoring systems (D. Sleeman and J.S. Brown, eds.), pp. 79–98, New York: Academic Press 1982.
Cercone, N.J. and McCalla, G.I.: There is no magic wand, but then again there is no smoking gun either. Symposium on Artificial and Natural Intelligence, Knowledge-Based Computer Systems Conference, Bombay, India 1989.
Cerri, S.A., Cheli, E. and Mclntyre, A.: “Nobile: user model acquisition in a natural laboratory”, to appear in M. Jones and P. H. Winne (eds.), Foundations and Frontiers of Adaptative Learning Environments, Springer Varlag. In press.
Chambers, J.A. and Sprecher, J.W.: Computer assisted instruction: current trends and critical issues. Communications of the ACM, 23, 6, 332–242 (1980).
Clancey, W.J.: Qualitative student models. In: Annual reviews of computer science, volume 1 (J.F. Traub, ed.). pp. 381–450, Palo Alto, California: Annual Reviews Inc. 1986.
Costa, E.: Machine learning, explanation-based learning, and ITS. In this volume.
dcKleer, J.: An assumption-based truth maintenance system. Artificial Intelligence, 28, 2,127–162 (1986).
deKleer, J. and Brown, J.S.: A qualitative physics based on confluences. Artificial Intelligence, 24, (1–3), 7–83 (1984).
diSessa, A. and Abelson, H.: BOXER: a constructible computational medium. Communications of the ACM, 29, 9 (1986).
Elsom-Cook, M.T.: Guided discovery tutoring and bounded user modelling. In: Artificial intelligence and human learning: intelligent computer-aided instruction (J. Self, ed.), pp. 165–178, London: Chapman and Hall 1988.
Elsom-Cook, M.T.(ed.): Guided discovery tutoring: a framework for ICAI research. London: Paul Chapman 1990.
Escott, J.A. and McCalla, G.I.: Problem solving by analogy: a source of errors in novice LISP programming. Proceedings of the Intelligent Tutoring Systems Conference, pp. 312–319, C. Frasson (Ed.) Univ. Montreal, Canada 1988.
Feurzeig, W. and Ritter, F.: Understanding reflective problem solving. In: Intelligent tutoring systems: lessons learned (J. Psotka, LX. Massey, S.A. Mutter, eds.), pp. 435–450. Hillsdale, New Jersey: Lawrence Erlbaum 1988.
Finin, T. and Drager, D.: GUMS1: a general user modelling system, Proceedings of the Sixth Canadian Society for Computational Studies of Intelligence Conference, pp. 24–30, Montreal, Canada 1986.
Gadwal, D.: UMRAO: a chess endgame tutor. (M.Sc. Thesis), TR 90–4, ARIES Laboratory, Department of Computational Science, University of Saskatchewan, Saskatoon, Canada 1990.
Gilmore, D. and Self, J. A.: The application of machine learning to intelligent tutoring systems. In: Artificial intelligence and human learning: intelligent computer-aided instruction (J. Self, ed.), pp. 179–196, London: Chapman and Hall 1988.
Goldstein, LP.: The genetic graph: a representation for the evolution of procedural knowledge. In: Intelligent tutoring systems (D.H. Sleeman and J.S. Brown, eds.), pp. 51–77. London: Academic Press 1982.
Greer, J.E. and McCalla, G.I.: A computational framework for granularity and its application to educational diagnosis. Proc. 11th International Joint Conference on Artificial Intelligence, pp. 477–482, Detroit, Michigan 1989.
Huang, X., McCalla, G.I., Neufeld, E., and Greer, J.E.: Revising deductive knowledge and stereotypical knowledge in a student model. User Modelling and User Adapted Interaction,, 1:87–115,1991.
Johnson, W.L. and Soloway, E.M.: PROUST: an automatic debugger for Pascal programs. Byte, 10, 4, 179–190 (1985).
Laird, J.E., Rosenbloom, P.S. & Newell, A.: Chunking in SOAR: the anatomy of a general learning mechanism. Machine Learning, 1,11–46 (1986).
Laurillard, D.: Generative student models or knowledge-based teaching strategies? In Adaptative learning environments (Winne P.H. and Jones M.L., eds.), NATO ASI Series F, Vol. 85. Berlin: Springer-Verlag 1992.
Lesgold, A.M., Bonar, J.G., Ivill, J.M., and Bowen, A.: An intelligent tutoring system for electronics troubleshooting: DC-circuit understanding. In: Knowing and learning: issues for the cognitive psychology of instruction (L.B. Rcsnick, ed.), New Jersey: Lawrence Erlbaum, 1987.
Mackworth, A.K. and Havens, W.S.: Structuring domain knowledge for visual perception. Proceedings of the Seventh International Joint Conference on Artificial Intelligence, pp. 625–627, Vancouver, Canada 1981.
Mark, M.: Evaluation of intelligent tutoring systems. TR 90–2, ARIES Laboratory, Department of Computational Science, University of Saskatchewan, Saskatoon, Canada 1990.
Martins, J.P.: Computational issues in belief revision, In this volume.
McAllester, D.A.: Truth maintenance, Proceedings of AAAI-90. pp. 1109–1115, Boston, Massachusetts, 1990.
McCalla, G.I.: Some issues for guided discovery tutoring research: granularity-based reasoning, student model maintenance, and pedagogical planning. TR 89–3, ARIES Laboratory, Department of Computational Science, University of Saskatchewan, Saskatoon, Canada (presented at the NATO Advanced Research Workshop on Guided Discovery Tutoring, Tuscany, Italy 1989).
McCalla, G.I. and Brecht, B.J.: Negotiated tutoring needs student modelling and instructional planning: arguments and experiences from the perspective of the SCENT project. In: R. Moyse and M. Elsom Cook (eds.), Knowledge Negotiation, Academic Press, London, 1992, pp. 41–68
McCalla, G.I., Bunt, R.B. and Harms, JJ.: The design of the SCENT automated advisor. Computational Intelligence, 2, 2, 76–92 (1986).
McCalla, G.I., Greer, J.E., and 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 and G. Gauthier, eds.). pp. 140–161, New Jersey: Ablex 1990.
McCalla, G.I., Greer, J.E., Barrie, B., and Pospisil, P.: Granularity hierarchies. International Journal on Computers and Mathematics, special issue on Semantic Networks, 23, 2–5, February 1992, pp. 363–376..
Mitchell, T.M.: Generalization as search. Artificial Intelligence, 18, 203–226 (1981).
Moebus, C: The relevance of computational models of knowledge acquisition for the design of helps in the problem solving monitor ABSYNT. Proceedings of the International Conference on Advanced Research on Computers in Education, pp. 57–64, Tokyo, Japan 1990.
Mohan, P.: Adapting instruction to the cognitive needs of learners in intelligent tutoring systems, M.Sc. Thesis Proposal, Department of Computational Science, University of Saskatchewan, Saskatoon, Canada 1990.
Moyse, R. and Elsom-Cook, M.(eds.), Knowledge Negotiation, Academic Press, London, 1992.
O’Malley, C. (ed.): Computer supported collaborative learning. NATO ASI Series F. Berlin: Springer-Verlag. In press.
Palthepu, S. and Greer, J.E.: The role of granularity in explanation-based generalisation. AAAI Workshop on Explanation, Boston, Massachusetts 1990. Private communication.
Papert, S.: Mindstorms: children, computers, and powerful ideas. New York: Basic Books 1980.
Reiser, B.J., Anderson, J.R., and Farrell, R.G.: Dynamic student modeling in an intelligent tutor for LISP programming. Proc. Ninth International Joint Conference on Artificial Intelligence, pp. 8 -14, Los Angeles, California 1985.
Reiser, B.J., Friedmann, P., Kimberg, D.Y., Ranney, M.: Constructing explanations from problem solving rules to guide the planning of programs. Proc. International Conference on Intelligent Tutoring Systems (ITS’88), pp. 222–229, Montreal 1988.
Sack, W.: Finding errors by overlooking them. In: Intelligent tutoring systems: at the crossroads of artificial intelligence and education (C. Frasson and G. Gauthier, cds.), pp. 206–233, New Jersey: Ablex 1990.
Self, J.A.: Student models in computer-aided instruction. International Journal of Man-Machine Studies, 6, 261–276 (1974).
Self, J.A.: The use of belief systems for student modelling. First European Congress on Artificial Intelligence and Training, Lille, France, 1988.
Self, J.A.: Bypassing the intractable problem of student modelling. In: Intelligent tutoring systems: at the crossroads of artificial intelligence and education (C. Frasson and G. Gauthier, cds.), pp. 107–123, New Jersey: Ablex 1990.
Shaw, M.: Problems of validation in a knowledge acquisition system using multiple experts. Proceedings of the Second European Knowledge Acquisition Workshop, pp. 5.1–15, Bonn, Germany 1988.
Shuell, T.: Designing instructional computing systems for meaningful learning. In Adaptative learning environments (Winne P.H. and ones M.L. J, eds.), NATO ASI Series F, Vol. 85. Berlin: Springer-Verlag 1992.
Shute, V.: Rose garden promises of intelligent tutoring systems: blossom or thorn? Space Operations, Applications and Research Symposium, Albuquerque, New Mexico 1990.
Shute, V. and Glaser, R.: A large-scale evaluation of an intelligent discovery world: Smithtown. Interactive Learning Environments, 1, 51–77 (1990).
Smith, R.: The alternate reality kit: an animated environment for creating interactive simulations. Proc. IEEE Workshop on Visual Languages, pp. 99–106, Dallas 1986.
VanLehn, K.: Student modelling. In: Foundations of intelligent tutoring systems (M.C. Poison and J.J. Richardson, eds.), pp. 55–78, Hillsdale, New Jersey: Lawrence Erlbaum 1988.
VanLehn, K.: Learning one subprocedure per lesson, Artificial Intelligence, 31, 1, 1–40 (1987).
Ward, B.: ET-SOAR: an electrostatics tutor. Ph.D. Thesis, Department of Computer Science, Carnegie-Mellon University, Pittsburgh, Pennsylvania forthcoming, May, 1992.
Winne, P.H.: Theories of instruction and of intelligence for designing artificially intelligent tutoring systems. Educational Psychologist, 24, 3, 229–259 (1989).
Winne, P.H.: A framework for designing and interpreting interactions between learners and instructional computing systems. In: In Adaptative learning environments (P.H. Winne and M.L. Jones, eds.), NATO ASI Scries F, Vol. 85. Berlin: Springer-Verlag 1992.
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McCalla, G.I. (1992). The Central Importance of Student Modelling to Intelligent Tutoring. In: Costa, E. (eds) New Directions for Intelligent Tutoring Systems. NATO ASI Series, vol 91. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77681-6_8
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