Skip to main content
SpringerLink
Log in

Student modelling in intelligent tutoring systems

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Student modelling is a special type of user modelling which is relevant to the adaptability of intelligent tutoring systems. This paper reviews the basic techniques which have been used in student modelling and discusses issues and approaches of current interest. The role of a student model in a tutoring system and methods for representing information about students are discussed. The paper concludes with an overview of some unresolved issues and problems in student modelling.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Bibliography

  • Anderson, J. R. (1983).The Architecture of Cognition. Harvard University Press, Cambridge, Massachusetts.

    Google Scholar 

  • Beard, M. H., Lorton, P. V., Searle, B. W. and Atkinson, R. C. (1973). Comparison of Student Performance and Attitude under Three Lesson Selection Strategies in Computer Assisted Instruction. Tech. report 222. Institute for Mathematical Studies in the Social Sciences, Stanford University.

  • Bellack, A., Kliebard, H., Hyman, R. and Smith, F. (1966).The Language of the Classroom. Teachers College Press, Columbia University, New York.

    Google Scholar 

  • Brown, J. S. and Van Lehn, K. (1980). Repair Theory: A Generative Theory of Bugs in Procedural Skills.Cognitive Science 4, 379–426.

    Google Scholar 

  • Brown, J. S., Burton, R. R., Hausman, C. L., Goldstein, I. P., Huggins, B. and Miller, M. L. (1977). Aspects of a Theory of Automated Student Modelling. Technical report 3549. Bolt, Beranek and Newman, Cambridge.

    Google Scholar 

  • Brown, J. S. and Burton, R. R. (1978). Diagnostic Models for Procedural Bugs in Mathematical Skills.Cognitive Science 2, 155–192.

    Google Scholar 

  • Burton, R. R. (1982). Diagnosing Bugs in a Simple Procedural Skill. In Sleeman, D. H. and Brown, J. S. (Eds.),Intelligent Tutoring Systems. Academic Press, London.

    Google Scholar 

  • Burton, R. R. and Brown, J. S. (1976). A Tutoring and Student Modelling Paradigm for Gaming Environments. ACM SIGCUE topics, Vol. 2.

  • Carbonell, J. R. (1970). Mixed-initiative Man-computer Instructional Dialogues. Tech. report No. 1971. Bolt, Beranek and Newman, Cambridge.

    Google Scholar 

  • Carr, B. and Goldstein, I. P. (1977). Overlays: A Theory of Modelling for Computer-aided instruction. AI lab memo 406. Massachusetts Institute of Technology, Cambridge.

    Google Scholar 

  • Cerri, S., Elsom-Cook, M. T. and Leoncini, M. (1990). TRILL: The Rather Intelligent Little Lisper.Intelligent Tutoring Media 1(1).

  • Clancey, W. J. (1986). Qualitative Student Models. In Traub, J. F.et al. (Eds.),Annual Review of Computer Science 1, 381–450.

    Google Scholar 

  • Cumming, G. D. and Self, J. A. (1991). Learner Models in Collaborative Intelligent Educational Systems. In Goodyear, P. (Ed.),Teaching Knowledge and Intelligent Tutoring. Ablex.

  • Elsom-Cook, M. T. (1988). Guided Discovery Tutoring and Bounded User Modelling. In Self, J. A. (Ed.),Artificial Intelligence and Human Learning. Capman and Hall, London.

    Google Scholar 

  • Elsom-Cook, M. T. (1990). Belief-based Bounded User Modelling. InLearning Technology in the European Communities. Kluwer, Dordrecht.

    Google Scholar 

  • Evertsz, R. and Elsom-Cook, M. T. (1990). Generating Critical Problems in Student Modelling. In Elsom-Cook, M. T. (Ed.),Guided Discovery Tutoring. Paul Chapman, London.

    Google Scholar 

  • Gilmour, D. and Self, J. (1988). The Application of Machine Learning to Intelligent Tutoring Systems. In Self, J. (Ed.),Artificial Intelligence and Human Learning. Chapman and Hall, London.

    Google Scholar 

  • Goldstein, I. P. (1982). The Genetic Graph: A Representation for the Evolution of Procedural Knowledge. In Sleeman, D. H. and Brown, J. S. (Eds.),Intelligent Tutoring Systems. Academic Press, London. Also inInternational Journal of Man-Machine Studies 11, 51–77.

    Google Scholar 

  • Hennessy, S. (1990). Why Bugs Are Not Enough. In Elsom-Cook, M. (Ed.),Guided Discovery Tutoring. Paul Chapman, London.

    Google Scholar 

  • Johnson, W. L. (1986).Intention-Based Diagnosis of Novices Programming Errors. Pitman, London.

    Google Scholar 

  • Laird, J. E., Newell, A. and Rosenbloom, P. S. (1987). SOAR: An Architecture for General Intelligence.Artificial Intelligence 33.

  • Langley, P. and Ohlsson, S. (1984). Automated Cognitive Modelling. InProceedings of AAAI — 84, pp. 193–197.

  • Mitchell, T. (1978). Version Spaces: An Approach to Concept Learning. Ph.D. Dissertation, Stanford University.

  • Ohlsson, S. (1985). Some Principles of Intelligent Tutoring.Instructional Science 14(3–4), 293–326. Also in Lawler, R. and Yazdani, M. (Eds.),Artificial Intelligence and Education. Ablex, Norwood, N.J.

    Google Scholar 

  • Payne, S. J. and Squibb, H. R. (1986). Understanding Algebra Errors: The Psychological Status of Mal-Rules. Tech. report no. 43. Centre for Research on Computers and Learning, Lancaster University.

  • Reiser, B. J., Anderson, J. R. and Farrell, R. G. (1985). Dynamic Student Modelling in an Intelligent Tutor for Lisp Programming.Proceedings of IJCAI 1985. Los Angeles.

  • Rich, E. A. (1979). User Modelling via Stereotypes.Cognitive Science 3, 329–354.

    Google Scholar 

  • Self, J. A. (1974). Student Models in Computer-aided Instruction.International Journal of Man-Machine Studies 6, 261–276.

    Google Scholar 

  • Self, J. A. (1979). Student Models and Artifical Intelligence.Computers and Education 3, 309–312.

    Google Scholar 

  • Self, J. A. (1986). The Application of Machine Learning to Student Modelling.Instructional Science 14, 327–338.

    Google Scholar 

  • Self, J. A. (1987). User Modelling in Open Learning Systems. In Whiting, J. and Bell, D. (Eds.),Tutoring in Monitoring Facilities in European Open Learning. Elsevier.

  • Self, J. A. (1988a). Knowledge, Belief and User Modelling. In O'Shea, T. and Sgurev, V. (Eds.),Artifical Intelligence III: Methodology, Systems, Applications. North-Holland.

  • Self, J. A. (1988b). Student Models — What User Are They?. In Ercoli, P. and Lewis, R. E. (Eds.),Artifical Intelligence Tools in Education. North-Holland.

  • Self, J. A. (1988c). The Use of Belief Systems for Student Modelling. Technical report. Centre for Research on Computers and Learning, University of Lancaster.

  • Self, J. A. (1989). The Case for Formalizing Student Models (and Intelligent Tutoring Systems Generally).Proceedings of Fourth International Conference on AI and Education. Amsterdam.

  • Self, J. A. (1990). Bypassing the Intractable Problem of Student Modelling. In Frasson, C. and Gauthier, G. (Eds.),Intelligent Tutoring Systems: At the Crossroads of AI and Education. Ablex.

  • Self, J. A. (1991). Formal Approaches to Student Modelling. In McCalla, G. and Greer, J. (Eds.),Student Modelling. Springer-Verlag (to appear).

  • Sleeman, D. (1983). Inferring Student Models for Intelligent Computer-Aided Instruction. In Michalski, R. Carbonell, J. and Mitchell, T. (Eds.),Machine Learning. Tioga Publishing Co.

  • Stevens, A. L. and Collins, A. (1979). Misconceptions in Students Understanding.International Journal of Man-Machine Studies 11, 145–156. Also in Sleeman, D. and Brown, J. S. (Eds.),Intelligent Tutoring Systems. Academic Press, London.

    Google Scholar 

  • Van Lehn, K. (1982). Bugs Are Not Enough: Empirical Studies of Bugs, Impasses and Repairs in Procedural Skills.Journal of Mathematical Behaviour 3, 3–72.

    Google Scholar 

  • Van Lehn, K. (1983a). Felicity Conditions for Human Skill Acquisition: Validating an AI-based Theory. Tech. Rep. No. C15-21. Xerox Research Center, Palo Alto, CA.

    Google Scholar 

  • Van Lehn, K. (1983b). On the Representation of Procedures in Repair Theory. In Ginsburg, H. P. (Ed.),The Development of Mathematical Thinking. Academic Press, London.

    Google Scholar 

  • Van Lehn, K. (1988). Towards a Theory of Impasse-driven Learning. In Mandl, H. and Lesgold, A. (Eds.),Learning Issues for Intelligent Tutoring Systems. Springer, New York.

    Google Scholar 

  • Van Lehn, K. (1990).Mind Bugs: The Origin of Procedural Misconceptions. MIT Press, Cambridge, MA.

    Google Scholar 

  • Van Lehn, K. (1991). Two Pseudo-students: Applications of Machine Learning to Formative Evaluation. In Lewis, R. and Otsuki, S. (Eds.),Advanced Research on Computers in Education. Elsevier Science Publishers.

  • Van Lehn, K. and Garlick, S. (1987). Cirrus: An Automated Protocol Analysis Tool.Proceedings of the Fourth Machine Learning Workshop. Morgan Kaufman, Los Altos, CA.

    Google Scholar 

  • Van Lehn, K., Jones, R. M. and Chi, M. T. H. (1991). Modelling the Self-explanation Effect with Cascade 3. In Hammond, K. and Gentner, D. (Eds.),Proceedings of the Thirteenth Annual Conference of the Cognitive Science Society. Erlbaum, Hillsdale, NJ.

    Google Scholar 

  • Whitehead, A. N. (1932).The Aims of Education. Benn, London.

    Google Scholar 

Download references

Authors
  1. Mark Elsom-Cook
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Electric Brain Company

Rights and permissions

Reprints and permissions

About this article

Cite this article

Elsom-Cook, M. Student modelling in intelligent tutoring systems. Artif Intell Rev 7, 227–240 (1993). https://doi.org/10.1007/BF00849556

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00849556

Key Words

Search

Navigation