Abstract
The applicability of Knowledge Space Theory (Falmagne and Doignon) and Bayesian Belief Networks (Pearl) as probabilistic student models imbedded in an Intelligent Tutoring System is examined. Student modeling issues such as knowledge representation, adaptive assessment, curriculum advancement, and student feedback are addressed. Several factors contribute to uncertainty in student modeling such as careless errors and lucky guesses, learning and forgetting, and unanticipated student response patterns. However, a probabilistic student model can represent uncertainty regarding the estimate of the student's knowledge and can be tested using empirical student data and established statistical techniques.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Andersen, S. K., Olesen, K. G., Jensen, F. V. & Jensen, F. (1989). HUGIN-A shell for building Bayesian belief universes for expert systems. In N. S. Sridharan (Ed.), Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, 2 (pp. 1080–1085). San Mateo, CA: Morgan Kaufmann.
Charniak, E. (1991) Bayesian networks without tears. AI Magazine, 12, (4), 50–63.
Doignon, J.-P., & Falmagne, J.-C. (1985). Spaces for the assessment of knowledge. International Journal of Man-Machine Studies, 23, 175–196.
Falmagne, J.-C. & Doignon, J.-P. (1988). A class of stochastic procedures for the assessment of knowledge. British Journal of Mathematical and Statistical Psychology, 41, 1–23.
Falmagne, J.-C., Koppen, M., Villano, M., Doignon, J.-P., Johannesen, L. (1990). Introduction to knowledge spaces: How to build, test, and search them. Psychological Review, 97(2), 201–224.
Kambouri, M., Koppen, M., Villano, M., & Falmagne, J.-C. (1992). Knowledge assessment: Tapping human expertise by the QUERY routine. Submitted for publication.
Morawski, P. Understanding Bayesian belief networks. (1988). AI Expert, May, 44–48.
Pearl, J. (1988). Probabilistic reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann.
Villano, M. (1991). Computerized Knowledge Assessment: Building the Knowledge Structure and Calibrating the Assessment Routine, (Doctoral dissertation, New York University, 1991).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1992 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Villano, M. (1992). Probabilistic student models: Bayesian Belief Networks and Knowledge Space Theory. In: Frasson, C., Gauthier, G., McCalla, G.I. (eds) Intelligent Tutoring Systems. ITS 1992. Lecture Notes in Computer Science, vol 608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55606-0_58
Download citation
DOI: https://doi.org/10.1007/3-540-55606-0_58
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-55606-0
Online ISBN: 978-3-540-47254-4
eBook Packages: Springer Book Archive