Diag-Skills: A Diagnosis System Using Belief Functions and Semantic Models in ITS

This work is related to the diagnosis process in intelligent tutoring systems (ITS). usually a complex task that relies on imperfect data. Indeed, learning data may suffer from imprecision, uncertainty, and sometimes contradictions. In this paper, we propose Diag-Skills model uses theory of belief functions capture these imperfections. The objective twofold: first, dynamic evaluated skills, then, prediction state non-evaluated ones. We conducted two studies evaluate precision Diag-Skills. evaluations showed good predictions almost perfect agreement with instructor when failed predict effective skill. Our main premise results will serve as support remediation feedbacks given learners by providing them proper personalization.