Probabilistic student models: Bayesian Belief Networks and Knowledge Space Theory
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.
KeywordsKnowledge Structure Intelligent Tutoring System Student Model Bayesian Belief Network Knowledge Assessment
Unable to display preview. Download preview PDF.
- 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.Google Scholar
- Charniak, E. (1991) Bayesian networks without tears. AI Magazine, 12, (4), 50–63.Google Scholar
- Doignon, J.-P., & Falmagne, J.-C. (1985). Spaces for the assessment of knowledge. International Journal of Man-Machine Studies, 23, 175–196.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Kambouri, M., Koppen, M., Villano, M., & Falmagne, J.-C. (1992). Knowledge assessment: Tapping human expertise by the QUERY routine. Submitted for publication.Google Scholar
- Morawski, P. Understanding Bayesian belief networks. (1988). AI Expert, May, 44–48.Google Scholar
- Pearl, J. (1988). Probabilistic reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann.Google Scholar
- Villano, M. (1991). Computerized Knowledge Assessment: Building the Knowledge Structure and Calibrating the Assessment Routine, (Doctoral dissertation, New York University, 1991).Google Scholar