Incremental and Adaptive Fuzzy Clustering for Virtual Learning Environments Data Analysis

Abstract

Virtual Learning Environments (VLE) offer a wide range of courses and learning supports for students. Such innovative learning platforms generate daily a huge quantity of data, regarding the interactions among the students and the VLE. To analyze these big educational data a new research branch called educational data mining (EDM) has emerged, that puts together computer scientists and pedagogues researchers' expertise. So far, educational data have been studied as stationary data by traditional machine learning methods. Rather, educational data are non-stationary in nature and can be better analyzed as data streams. In this paper we investigate the use of an adaptive fuzzy clustering algorithm called DISSFCM (Dynamic Incremental Semi-Supervised FCM) to process educational data as data streams and predict the students' outcomes to one exam module. Numerical experiments on the Open University Learning Analytics Dataset (OULAD) show the reliability of DISSFCM in creating good classification models of educational data.