出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：In the globalised education sector, predicting student performance has become a central issuefor data mining and machine learning researchers where numerous aspects influence thepredictive models. This paper attempts to apply classification algorithms to evaluate student’sperformance in the higher education sector and identify the key features affecting the predictionprocess based on a combination of three major attributes categories. These are: admissioninformation, 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 level2studentdatasets at Brunel University London for the academic year 2015/16. The outcome ofthe predictive model identifies the low, medium and high risk of failure of students. Thisprediction will help instructors to assist high-risk students by making appropriate interventions.
