Bayesian based student knowledge modeling in intelligent tutoring systems

In this paper we present student knowledge modeling algorithm in a probabilistic domain within an intelligent tutoring system. The student answers to questions requiring diagnosing skills are used to estimate the actual student model. Updating and verification of the model are conducted based on the matching between the student's and model answers. Three different approaches to updating are suggested, namely coarse, refined, and blended updating. In addition, different granularity levels are evaluated by changing the value of the updating step and the output of this parametric study is indicated. Results suggest that the refined model provides better approximation of the student model while utilizing blended model decreases the required trial numbers to model the student knowledge with limited reduction in accuracy.