Students’ Academic Performance Prediction Using Educational Data Mining and Machine Learning: A Systematic Review

Forecasting the performance of students holds paramount importance in the context of higher education since the criteria for a high quality university is based on its excellent record of academic achievements. At the present time, predicting students’ performance becomes more challenging due to the huge increase in the amount of educational data that is now available in educational databases. With the introduction of information systems and data mining and machine learning techniques in education a new era has been started to reveal the methodologies in studying and analyzing students’ academic performance and to enable the recording and retention of large volumes of data in educational institutions. This paper seeks to systematically review the current research on predicting student performance through the use of educational data mining and machine learning techniques. The review synthesizes a wide range of studies, encompassing diverse educational levels, data sources, and predictive models. A comprehensive review was conducted for available research spanning from 2015 to 2023, to provide a foundational understanding of the intelligent methods employed in forecasting student performance. The search encompassed different electronic bibliographic databases, such as IEEE Xplore, Google Scholar, and Science Direct. In this paper, 17 survey papers and 74 research papers have been examined and analyzed, emphasizing seven key aspects that aim to have interpretable models for forecasting student performance.