A Qualitative Comparison of Techniques for Student Modelling in Intelligent Tutoring Systems

Abstract: Wise Tutoring Systems (ITS) are intuitive learning conditions dependent on guidance helped by P.C.s. The insight of these frameworks is, to a great extent, ascribed to their capacity to adjust to a particular understudy during the educating cycle. As a rule, the variation cycle depicts by three stages: (I) getting the data about the understudy, (ii) preparing the data to introduce and refresh an understudy model, also, (iii) utilizing the understudy model to give the transformation. In this paper, we considered viewpoints related to understudy displaying (S.M.) in Intelligent Tutoring Systems. First, we make a subjective examination of two procedures: Bayesian Networks (B.N.) and Case-based Reasoning (CBR) for S.M. We apply the two strategies to a contextual analysis concerning the advancement of an ITS for e-learning in the clinical space. At last, we talk about the outcomes acquired. Index Terms: Bayesian Networks, Case-based Reasoning, Intelligent Tutoring Systems, Student Model.