Students Behavioural Analysis in an Online Learning Environment Using Data Mining (ICIAfS)
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.
Cite
@article{arxiv.1412.7813,
title = {Students Behavioural Analysis in an Online Learning Environment Using Data Mining (ICIAfS)},
author = {I. P. Ratnapala and R. G. Ragel and S. Deegalla},
journal= {arXiv preprint arXiv:1412.7813},
year = {2014}
}
Comments
appears in The 7th International Conference on Information and Automation for Sustainability (ICIAfS) 2014