A case-based reasoning approach to intelligent tutoring system by considering learner style model

Abstract

We propose an intelligent tutoring system, which uses the parameters of learner model for personalizing the essential courseware in a certain field. Pattern analysis amp; understanding has been selected as a platform for both implementing our approach, since it can be applied equally to a wide range of engineering branches, and can, at the same time, be used as a systemic discipline for nonengineering areas as well. The learner style model that has been used in our system is based on Dunn and Dunn, Kolb, and Myers-Briggs theories. Having the learner models of different users, together with the suitable arrangements of the essential courseware, we will show how a case-based reasoning approach based on a process of case adaptation can yield producing a novel courseware arrangement.