Semantic web based learning styles identification for social learning environments personalization

Abstract

Personal learning is a hot research topic in the field of web-based learning systems as there is no one appropriate learning path for all students. Many researchers are using semantic web technologies to find new approaches to develop personalized learning environments based on describing knowledge using ontologies. In this paper, we investigate the personalization of learning process taking advantage of the Social Semantic Web, Learning Styles and Bayesian Networks to provide students with recommendations of collaborators and relevant resources that best fit their needs. The proposed personalization approach is based on discovering students' learning styles by means of an analysing of their behaviours. Semantic Web concepts are applied to describe pertinent entities and variables of the proposed Bayesian Network, defined by use of inference mechanisms. Some experiments were conducted and results on students' learning styles estimation are compared with those obtained by use of MBTI questionnaire.