Inferring the Differential Student Model in a Probabilistic Domain Using Abduction inference in Bayesian networks.

In this paper we aim to estimate the differential student knowledge model in a probabilistic domain within an intelligent tutoring system. The suggested algorithm aims to estimate the actual student model through the student answers to questions requiring diagnosing skills. Updating and verification of the model are conducted based on the matching between the student and model answers. Two different approaches to updating namely coarse and refined model are suggested. Results suggest that the refined model, although takes more computational resources, provides a slightly better approximation of the student model.