An Evaluation of Student Performance at e-Learning Platform

Abstract

E-learning represents novel learning way, which increase teaching flexibility and availability of learning resources. This paper explores the evaluation of student success at e-learning platform. Authors used multi-method approach for data analysis (i.e. Social Network Analysis, K-means Clustering and Linear Regression). This approach presents novelty in the field of e-learning, which provides more detailed analysis that enable more relevant results. The research was conducted with student group at the University of Novi Sad, Faculty of Technical Sciences, Serbia. Results indicate that digital resources at the e-learning platform make strong effects on student success. Moreover, results indicate that students with the similar grades belongs to the same clusters.