Utilizing Diagnosing Problems in a Probabilistic Domain to Build Student Models

In this paper we aim to estimate the differential student knowledge model 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. Two different approaches to updating are suggested, i) coarse and ii) refined model updating. Moreover, the effect of the order of which questions are presented to the student is investigated. Results suggest that the refined model, although takes more computational resources, provides a slightly better approximation of the student model. In addition, the accuracy of the algorithm is highly insensitive to the order of which the questions are presented, more so when using the refined model updating approach.