Student’s Performance Based on E-Learning Platform Behaviour using Clustering Techniques

Abstract

Measuring student engagement on e-learning platforms is critical to student learning because online platforms can make some students feel isolated and disconnected, leading to loss of interest and affecting academic performance. This paper explores the relation between student's responses on e-learning platforms and performance of various skill levels. Using K-Means clustering technique the students are grouped by their total responses and relative total skill levels to determine cluster having various total response rate and total skill levels in order to measure student performance based on their engagement. This will also assist instructors to focus on students/clusters that require more attention in the teaching and learning process. The clustering results indicate students with higher engagement tend to perform better compared to the cluster with moderate and lower engagement patterns