Probabilistic Student Modeling with Knowledge Space Theory

Abstract : This article presents Knowledge Space Theory (Falmagne and Doignon) as the foundation for a probabilistic student model to be imbedded in an intelligent Tutoring System (ITS). Applications to typical ITS student modeling issues such as knowledge representation, adaptive assessment, curriculum representation, advancement criteria, and student feedback are discussed. 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.