CLARISSE: A Machine Learning Tool to Initialize Student Models

  • Esma Aïmeur
  • Gilles Brassard
  • Hugo Dufort
  • Sébastien Gambs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2363)


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.


Cognitive Model Quantum Cryptography Quantum Information Processing Intelligent Tutor System Descriptor Space 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 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. 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. 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. 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. 5.
    Bennett, C.H., Brassard, G. and Ekert, A.K., “Quantum Cryptography”, Scientific American, pp. 164–171, October 1992.Google Scholar
  6. 6.
    Biswas, G., Weinberg, J. and Fisher, D., “ITERATE: A Conceptual Clustering Algorithm that Produces Cohesive Clusters”, 1995, available on the Internet at URL:
  7. 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. 8.
    Chuang, I.L. and Nielsen, M.A., Quantum Computation and Quantum Information, Cambridge University Press, 2000.Google Scholar
  9. 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. 10.
    Fisher, D., “Knowledge Acquisition via Incremental Conceptual Clustering”, Machine Learning 2, pp. 139–172, 1987.Google Scholar
  11. 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. 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. 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. 14.
    Kay, J., “Stereotypes, Students Models and Scrutability”, Proceedings of Intelligent Tutoring Systems: ITS 2000, pp. 19–30, 2000.Google Scholar
  15. 15.
    Lebowitz, M., “Experiments with Incremental Concept Formation: UNIMEM”, Machine Learning 2, pp. 103–138, 1987.Google Scholar
  16. 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. 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
  18. 18.
    Shor, P.W., “Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer”, SIAM Journal on Computing 26, pp. 1484–1509, 1997.zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Esma Aïmeur
    • 1
  • Gilles Brassard
    • 1
  • Hugo Dufort
    • 2
  • Sébastien Gambs
    • 1
  1. 1.Département IROUniversité de MontréalMontréal (Québec)Canada
  2. 2.Netvention Inc.Montreal (Québec)Canada

Personalised recommendations