Predicting academic success in higher education: literature review and best practices

• Published in: International Journal of Educational Technology in Higher Education Vol. 17; no. 1; pp. 1 - 21
• Main Authors: Alyahyan, Eyman, Düştegör, Dilek
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 10.02.2020; Springer Nature B.V; SpringerOpen
• Subjects: Academic achievement; Artificial intelligence; At Risk Students; Computer Appl. in Social and Behavioral Sciences; Computer Science; Computers and Education; Data mining; Decisions; Educational Technology; Guidelines; Higher Education; Humanities; Information Systems Applications (incl.Internet); Law; Literature reviews; Machine learning; Prediction; Predictions; Review; Review Article; State-of-the-art reviews; Statistics for Social Sciences; Student success; Success; Teachers; Teaching Methods; Student success; Review; Higher education; Data mining; Prediction; Guidelines

Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively use...