Abstract
The student model is the key to providing adequate assistance in teaching and adaptive instruction by Intelligent Tutoring Systems (ITS). Student modelling has been recognized as a complex and difficult but important task by researchers. We propose a new approach to student modelling based on Case-Based Reasoning (CBR), which is simple and does not require computationally expensive inference algorithms. This paper presents the application of this approach in developing an ITS, which analyzes the student’s problem solving ability in order to obtain the knowledge component of the student model. We apply the formalism of Graph with Classified Concepts and Relations (GCR), an extended model of conceptual graph previously defined by us, to represent the problems, the cases, and the knowledge component of the student model in such systems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Anderson, J.R., Boyle, C. F., Corbett, A.T., and Lewis, M. W. Cognitive modeling and intelligent tutoring. Artificial Intelligence, 42, pages 7–49, 1990. 394
Anderson, J.R., Corbett, A.T., Koedinger, K., and Pelletier, R. Cognitive tutors: Lessons learned. The Journal of the Learning Sciences vol. 4, no. 2, pages 167–207, 1995. 394
Bauer, M. A dempster-shafer approach to modeling agent references for plan recognition. User Modelling and User-Adapted Interaction, 5, pages 317–348, 1996. 394
Gagné, R. M. The condition of learning and the theory of instruction. CBS College Publishing, Fourth ed., 1985. 395
Hawkes, L. W., Derry, S. J., and Rundensteiner, E. A. Individualized tutoring using an intelligent fuzzy temporal relational database. International Journal of Man-Machine Studies, 33, pages 409–429, 1990. 394
Holt, P., Dubs, S., Jones, M., and Greer, J. The state of student modelling. In Greer, J.E. and McCalla, G.I. (Eds): Student Modelling: The Key to Individualized Knowledge-Based Instruction, pages 3–35, NATO-ASI Series F, Springer-Verlag, 1994. 394
Jameson, A. Numerical uncertainty management in user and student modeling: An overview of systems and issues. User Modelling and User-Adapted Interaction, 5, pages 193–251, 1996. 394
Kolodner, J. Case-Based Reasoning. Morgan Kaufmann Publisher Inc., San Mateo CA, 1993. 395, 397
Leake, D.B. Case-Based Reasoning: Experiences, Lessons and Future Directions. AAAI Press, Menlo Park, California, 1996. 395
Petrushin, V. A. and Sinista, K. M. Using probabilistic reasoning techniques for learner modelling. World Conference on AI in Education, Edinburgh, Scotland, pages 418–425, 1993. 394
Schank, R. and Edelson, D. A role for AI in education: using technology to reshape education. Journal of Artificial Intelligence in Education, 1(2), pages 3–20, 1990. 395
Sowa, J.F. Principles of Semantic Network: Exploration in the Representation of Knowledge. Morgan Kaufmann Publisher Inc., San Mateo CA, 1991. 395
Villano, M. Probabilistic students models: bayesian belief networks and knowledge space theory. In Second International Conference on Intelligent Tutoring System, pages 491–498, Montréal, Canada, 1992. 394
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shiri, M.E., Aïmeur, A.E., Frasson, C. (1998). Student Modelling by Case Based Reasoning. In: Goettl, B.P., Halff, H.M., Redfield, C.L., Shute, V.J. (eds) Intelligent Tutoring Systems. ITS 1998. Lecture Notes in Computer Science, vol 1452. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-68716-5_45
Download citation
DOI: https://doi.org/10.1007/3-540-68716-5_45
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64770-6
Online ISBN: 978-3-540-68716-0
eBook Packages: Springer Book Archive