Unsupervised learning based mining of academic data sets for students’ performance analysis

The main purpose of the Educational Data Mining domain is to provide additional insights into the students’ learning mechanism and thus to offer a better understanding of the educational processes. This paper investigates the usefulness of unsupervised machine learning methods, particularly principal component analysis and relational association rule mining in analysing students’ academic performance data, with the broader goal of developing supervised learning models for students’ performance prediction. Experiments performed on a real academic data set highlight the potential of unsupervised learning models for uncovering meaningful patterns within educational data, patterns which will be relevant for predicting the students’ academic performance.