Mashael Al luhaybi, Allan Tucker and Leila Yousefi, Brunel University, UK

In the globalised education sector, predicting student performance has become a central issue for data mining and machine learning researchers where numerous aspects influence the predictive models. This paper attempts to apply classification algorithms to evaluate student’s performance in the higher education sector and identify the key features affecting the prediction process based on a combination of three major attributes categories. These are: admission information, module-related data and 1st year final grades. For this purpose, J48 (C4.5) decision tree and Naïve Bayes classification algorithms are applied on computer science level 2studentdatasets at Brunel University London for the academic year 2015/16. The outcome of the predictive model identifies the low, medium and high risk of failure of students. This prediction will help instructors to assist high-risk students by making appropriate interventions.

Prediction, classification, decision tree, Naïve Bayes, student performance