Information Theoretic Clustering for an Intelligent Multilingual Tutoring System

Abstract

People working as groups, collaborating, rather than people working individually, has unquestionably helped them develop and make accomplishments beyond our imagination. It is quite common to believe that human beings have an inner need to act as social beings. In computer science and particularly in intelligent tutoring systems, the related scientific literature enhances the conviction that even in learning, humans as students may improve the way they learn when working in groups and being in clusters. In this paper we present the information theoretic clustering built up in the context of student collaboration. Collaborative student groups are created with respect to the corresponding user models.