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Nowadays, researchers from numerous domains are very interested in increasing the quality of learning in educational institutions in order to improve student success and learning outcomes. Several studies have been made to predict student achievement at various levels. Most of the previous studies were focused on predicting student performance at graduation time or at the level of a specific course. The main objective of this paper is to highlight the recently published studies for predicting student academic performance in higher education. Moreover, this study aims to identify the most commonly used techniques for predicting the student's academic level. In addition, this study summarized the highest influential features used for predicting the student academic performance where identifying the most influential factors on student\u2019s performance level will help the student as well as the policymakers and will give detailed insights into the problem. Finally, the results showed that the RF and ensemble model were the most accurate models as they outperformed other models in many previous studies. In addition, researchers in previous studies did not agree on whether the admission requirements have a strong relationship with students' achievement or not, indicating the need to address this issue. Moreover, it has been noticed that there are few studies which predict the student academic performance using students\u2019 data in arts and humanities major.<\/jats:p>","DOI":"10.1155\/2022\/8924028","type":"journal-article","created":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T04:05:10Z","timestamp":1663646710000},"page":"1-26","source":"Crossref","is-referenced-by-count":37,"title":["Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3060-5022","authenticated-orcid":true,"given":"Sarah A.","family":"Alwarthan","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. 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