A data mining approach using unsupervised learning for profiling students

Abstract

The paper presents a data mining approach using unsupervised learning for profiling students. Unsupervised learning specifically the K-means clustering algorithm is applied to obtain clusters with similar patterns and characteristics. The clustering experiments were performed using academic background, parental support, and learning behavioural features as attributes. The characteristics that distinguish students belonging to those different clusters were examined. The findings uncovered the key characteristics of students’ performance, and it is helpful for future prediction. Appropriate learning support and intervention could be provided to tailor to the individual cluster of students to enhance their performance. The clustering algorithm also serves as a potential benchmark to monitor the progress of students’ performance and helps teachers to improve the course success.