Probabilistic student models: Bayesian Belief Networks and Knowledge Space Theory

The applicability of Knowledge Space Theory and Bayesian Belief Networks as probabilistic student models imbedded in an Intelligent Tutoring System is examined and student modeling issues such as knowledge representation, adaptive assessment, curriculum advancement, and student feedback are addressed.

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.