Students Behavioural Analysis in an Online Learning Environment Using Data Mining (ICIAfS)

TL;DR

This study applies data mining techniques to analyze student interaction patterns in online learning environments, revealing significant behavioral clusters and differences between graded and non-graded courses.

Contribution

It introduces a clustering-based analysis of student access behavior in online courses, highlighting passive learners and environment-based behavioral differences.

Findings

Over 40% of students are passive online learners.

Learning environment influences student access behavior.

Five distinct access groups identified among students.

Abstract

The focus of this research was to use Educational Data Mining (EDM) techniques to conduct a quantitative analysis of students interaction with an e-learning system through instructor-led non-graded and graded courses. This exercise is useful for establishing a guideline for a series of online short courses for them. A group of 412 students' access behaviour in an e-learning system were analysed and they were grouped into clusters using K-Means clustering method according to their course access log records. The results explained that more than 40% from the student group are passive online learners in both graded and non-graded learning environments. The result showed that the difference in the learning environments could change the online access behaviour of a student group. Clustering divided the student population into five access groups based on their course access behaviour. Among these groups, the least access group (NG-41% and G-42%) and the highest access group (NG-9% and G-5%) could be identified very clearly due to their access variation from the rest of the groups.