A Data Mining Approach to the Analysis of Students’ Learning Styles in an e-Learning Community: A Case Study

Abstract

In recent years, there has been a radical change in the world of education and training that is causing that many schools, universities and companies are adopting the most modern technologies, mainly based on Web architectures and Web 2.0 instruments and tools, for learning, managing and sharing of knowledge. In this context, an e-Learning system can reach its maximum potential and effectiveness if it could take advantage of the information in its possession and process it in an intelligent and personalized way. The Educational Data Mining is an emergent field of research where the approach to personalization makes use of the log data generated by learners during their training process, to dynamically update users learning profiles such as skills and learning styles and identify students behavioral patterns. In this paper we present a case study of a data mining approach, based on cluster analysis, in order to support the detection of learning styles in a community of learners, following the Grasha-Riechmann learning styles model. As an e-learning framework we used the Moodle LMS platform and studied the log files generated by a course taken by a community of learners. The first experimental results suggest a connection between clusters and learning styles, reinforcing the use of this approach.