CLARISSE: A Machine Learning Tool to Initialize Student Models
The initialization of the student model in an intelligent tutoring system is a crucial issue. It is not realistic to assume that each new student has the same prior knowledge concerning the topic being taught, be it nothing or some “standard” prior knowledge. We introduce CLARISSE, which is a novel categorization method. We illustrate this tool with the identification of categories among students for QUANTI, an intelligent tutoring system for the teaching of quantum information processing. In order to classify a new learner, CLARISSE generates an adaptive pre-test that can identify with high accuracy the learner’s category after very few questions.
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- 1.Aïmeur, E., Blanchard, E., Brassard, G., Fusade, B. and Gambs, S., “Designing a Multidisciplinary Curriculum for Quantum Information Processing”, Proceedings of Artificial Intelligence in Education: AI-ED’01, pp. 524–526, 2001.Google Scholar
- 2.Aïmeur, E., Blanchard, E., Brassard, G. and Gambs, S., “QUANTI: A Multidisciplinary Knowledge-Based System for Quantum Information Processing”, Proceedings of International Conference on Computer Aided Learning in Engineering Education: CALIE’01, pp. 51–57, 2001.Google Scholar
- 3.Arroyo, I., Conejo, R., Guzmand, E. and Woolf, B.P., “An Adaptive Web-based Component for Cognitive Ability Estimation”, Proceedings of Artificial Intelligence in Education: AI-ED’01, pp. 456–466, 2001.Google Scholar
- 4.Bauer, M., Gmytrasiewicz, P. and Pohl, W., Workshop “Machine Learning for User Modeling”, Proceedings of the Seventh International Conference on User Modeling, 1999.Google Scholar
- 5.Bennett, C.H., Brassard, G. and Ekert, A.K., “Quantum Cryptography”, Scientific American, pp. 164–171, October 1992.Google Scholar
- 6.Biswas, G., Weinberg, J. and Fisher, D., “ITERATE: A Conceptual Clustering Algorithm that Produces Cohesive Clusters”, 1995, available on the Internet at URL: http://cswww.vuse.vanderbilt.edu/~biswas/Papers/kdd/iterate-oilabs.html.
- 7.Bloedorn, E., Mani, I. and MacMillan, T.R., “Machine Learning of User Profiles: Representational Issues”, Proceedings of the National Conference on Artificial Intelligence, pp. 433–438, 1997.Google Scholar
- 8.Chuang, I.L. and Nielsen, M.A., Quantum Computation and Quantum Information, Cambridge University Press, 2000.Google Scholar
- 9.De Koning, K., and Bredeweg, B., “Exploiting Model-Based Reasoning in Educational Systems”, in Smart Machines in Education, K.D. Forbus and P.J. Feltovich (Eds), pp. 299–330, 2001.Google Scholar
- 10.Fisher, D., “Knowledge Acquisition via Incremental Conceptual Clustering”, Machine Learning 2, pp. 139–172, 1987.Google Scholar
- 11.Dufort, H., “Evaluation et adaptation automatique de cours dans un système tutoriel intelligent, Masters thesis under the direction of Esma Aïmeur, Université de Montréal, 1999.Google Scholar
- 12.Gluck, M.A. and Corter, J.E., “Information, Uncertainty, and the Utility of Categories”, Proceedings of the Seventh Annual Conference of the Cognitive Science Society, Hillsdale: Lawrence Erlbaum Associates, pp. 283–287, 1985.Google Scholar
- 13.Hanson S.J., “Conceptual Clustering and Categorisation: Bridging the Gap between Induction and Causal Models”, Machine Learning: An Artificial Intelligence Approach 3, 1990.Google Scholar
- 14.Kay, J., “Stereotypes, Students Models and Scrutability”, Proceedings of Intelligent Tutoring Systems: ITS 2000, pp. 19–30, 2000.Google Scholar
- 15.Lebowitz, M., “Experiments with Incremental Concept Formation: UNIMEM”, Machine Learning 2, pp. 103–138, 1987.Google Scholar
- 16.Millàn, E., Pérez-de-la-Cruz, J.L. and Svàzer, E., “Adaptive Bayesian Networks for Multilevel Student Modelling”, Proceedings of Intelligent Tutoring Systems: ITS 2000, pp. 534–543, 2000.Google Scholar
- 17.Raskutti, B. and Beitz, A., “Acquiring User Preferences for Information Filtering in Interactive Multi-Media Services”, Proceedings ofPRICAI, pp. 47–58, 1996.Google Scholar