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Attributes of Low Performing Students in E-Learning System Using Clustering Technique

Educational data mining is a pertinent and fast growing area in data mining. Additionally, the growth of e-Learning in recent times has created massive data available with free access to datasets online. Researchers have continuously evaluated these available data to produce vital knowledge, which has shown improvement in the education sector. Several research in educational data mining focuses on improving learning methods or students performance. Most of the research looks towards predicting students score or grade; however, this paper offers a different method of mining knowledge by looking at similar attributes of low-performing students in the available dataset. This approach is to group students with low grades and discover the main attributes of this category of students. With the knowledge acquired, stakeholders can look out for these attributes in their current and prospective students. This research collected data from an e-Learning system called Kalboard 360 for knowledge mining and makes use of the MakeDensityBasedClusterer technique embedded in WEKA tool to group the low performing students into two. The knowledge gained from the mining process shows that lower-level students that show regular absence in school with parents that are not participating earnestly in their learning process are most likely to perform poorly in their studies.