S PRAR: A novel relational association rule mining classification model applied for academic performance prediction

Abstract

This paper analyses the problem of predicting students’ academic performance, a subject that is increasingly investigated within the Educational Data Mining literature. For a better understanding of the educational related phenomena, there is a continuous interest in applying supervised and unsupervised learning methods for obtaining additional insights into the students’ learning process. The problem of predicting if a student will pass or fail at a certain academic discipline based on the students’ grades received during the semester is a difficult one, highly dependent on various conditions such as the course, the number of examinations during the semester, the instructors and their exigences. We propose a new classification model, S PRAR (Students Performance prediction using Relational Association Rules) for predicting the final result of a student at a certain academic discipline using relational association rules (RARs). RARs extend the classical association rules for expressing various relationships between data attributes. Experiments are performed on three real academic data sets collected from Babeş-Bolyai University from Romania. The performance of the S PRAR classifier on the considered case studies is compared against existing related work, being superior to previously proposed students’ performance predictors.