Clustering Methods Analysis in the E-Learning

Abstract

In this paper, the analysis of students who were part of the distance learning program has been done using various educational data mining clustering algorithms on the basis of their performance and activities carried out as a part of the process. Clusters have been used to find the relation between attributes (activities involved in the E-learning process such as downloading of study related material, exchange of messages with tutors and colleagues and posting in the discussion forum) and the final grades of students, and to find which characteristics mostly affect the classes. To sort out this, we have used various data mining techniques such as Agglomerative Hierarchical Clustering (using Ward method), and Non-Hierarchical clustering methods such as KMeans, KMeans ++, and CMeans The implementation of these algorithms is done in R language. The comparison of Hierarchical and non-Hierarchical clustering methods is made to find the most efficient algorithm.