Prediction of Academic Outcomes Using Machine Learning Techniques: A Survey of Findings on Higher Education

The growth of electronic data in educational institutions provides an opportunity to extract information that can be used to predict students’ academic performance and dropout rates. This paper provides a survey to explore the current state of research on academic performance prediction using machine learning techniques. A systematic literature search was conducted to identify relevant studies published between 2019 and 2023. The review analyzed studies that used various machine learning algorithms to predict academic performance in different educational contexts. The findings indicate that machine learning models can accurately predict academic performance with a high degree of precision, using a variety of variables such as demographic data, academic history, and student interaction. The review also highlights the challenges of the current research, including the need for collection and preprocessing procedures, and the importance of considering ethical implications related to the use of student data. The findings have important implications for educators, managers, and researchers interested in using machine learning techniques to promote student success.