In this paper we aim to estimate the differential student knowledge model in a probabilistic domain withinan intelligent tutoring system. The student answers to questions requiring diagnosing skills are used toestimate the actual student model. Updating and verification of the model are conducted based on thematching 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 tothe student is investigated. Results suggest that the refined model, although takes more computationalresources, provides a slightly better approximation of the student model. In addition, the accuracy of thealgorithm is highly insensitive to the order of which the questions are presented, more so when using therefined model updating approach..