Application of Machine Learning Method for Students’ Learning Behaviour Analysis in Moodle Learning Management System

Abstract

Abstract: - This research investigates the performance of K-Means Clustering Method to analyze students’ e-learning activities with the aim to identify clusters of students who use the e-learning environment in similar ways using data obtained from the log files of their actions as input. The K-Means clustering technique was used to group 53 Computer Science Students from Universiti Teknologi Malaysia into three clusters. Learning behaviors of students in each cluster were analyzed; a relationship between students’ learning behaviour and their academic performance (Final Results) was investigated. The analysis shows that students in Cluster1, having the highest interactions frequency with the e-learning, also got the highest final score mean of 91.12%, this followed by Students in Cluster2 with less number of interactions than Cluster1 and final score mean of 75.65%. Finally, students in Cluster3 have least number of interactions than the remaining clusters with least final score mean of 36.57%. The research shows that, students who participate more in Forum activities perform higher, while students with lowest records in Forum activities have the lowest performance. The research found that Forum activity has significant factor on student’s course success but it is optional to students and no marks allocated to it. The research suggests that marks should be allocated to Forum activities to encourage students’ participations.