A Learning Analytics Approach Using Clustering Data Mining for Learners Profiling to Extrapolate e-Learning Behaviours
Abstract: The study aims to gain insights into the patterns related to the diversity of learners and evaluate the relationship between learners’ factors and their academic performance. The pattern discovery was performed by applying clustering data mining to obtain typologies of the learners based on the academic records and e-learning interactions behaviour feature category. In this study, the k-means clustering unsupervised machine learning algorithm was applied to obtain the clusters. The clustering analysis of learners’ academic patterns and interactions behavioural patterns between learner sub-populations allows for a better understanding of how the learners behave and achieve. The clustering results identified similar group learners, and the learners could be provided with appropriate educational supports and approaches with the aim to enhance learners’ learning experience. The findings of this study are also useful to understand the effects of different features on learners’ academic performance specifically in an e-learning environment.