2014/12/25 by I. P. Ratnapala, Ratnapala, I. P., Roshan Ragel +5
Computer Science · Social Sciences · #Computers and Society (cs.CY) #Data Stream Mining Techniques #FOS: Computer and information sciences #Online Learning and Analytics #Online and Blended Learning #cs.CY
paper · pdf · doi:10.48550/arxiv.1412.7813
appears in The 7th International Conference on Information and Automation for Sustainability (ICIAfS) 2014
arxiv created 2014/12/25 · openalex publication_date 2014/12/25 · arxiv updated 2014/12/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.