Predicting engineering student success using machine learning

Abstract: Recent years have seen an increase in the number of students from diverse backgrounds enrolling into South African universities, presenting many challenges. Some students struggle with their academic choices, and universities struggle to understand and address the individual needs of such a diverse student base. Fortunately, vast amounts of student information have been collected and stored, giving an opportunity for researchers in educational data mining to derive some useful insights from this data to help both the universities and students. This research aims to identify factors that contribute to the success and or failure of a student, then predict the future performance of the student at enrolment. By using data pre-processing techniques, the experiments identify the most significant success factors from the data at enrolment time. The most significant factors can then be used to identify students who may need extra support, and the nature of those factors can help determine the manner of support needed. This study implemented and evaluated the effectiveness of the most commonly used and new machine learning algorithms in predicting student performance on a sample of 1366 engineering students. The results show various degrees of success in predicting student performance, and it is hoped that these findings will guide the selection of machine learning algorithms for future studies