Students’ academic performance and dropout predictions: a review / Ahmed O. Ameen, Moshood A. Alarape and Kayode S. Adewole

O. Ameen, Ahmed and A. Alarape, Moshood and S. Adewole, Kayode (2019) Students’ academic performance and dropout predictions: a review / Ahmed O. Ameen, Moshood A. Alarape and Kayode S. Adewole. Malaysian Journal of Computing (MJoC), 4 (2): 3. pp. 278-303. ISSN 2600-8238


Students’ Academic Performance (SAP) is an important metric in determining the status of students in any academic institution. It allows the instructors and other education managers to get an accurate evaluation of the students in different courses in a particular semester and also serve as an indicator to the students to review their strategies for better performance in the subsequent semesters. Predicting SAP is therefore important to help learners in obtaining the best from their studies. A number of researches in Educational Psychology (EP), Learning Analytics (LA) and Educational Data Mining (EDM) has been carried out to study and predict SAP, most especially in determining failures or dropouts with the goal of preventing the occurrence of the negative final outcome. This paper presents a comprehensive review of related studies that deal with SAP and dropout predictions. To group the studies, this review proposes taxonomy of the methods and features used in the literature for SAP and dropout prediction. The paper identifies some key issues and challenges for SAP and dropout predictions that require substantial research efforts. Limitations of the existing approaches for SAP and dropout prediction are identified. Finally, the paper exposes the current research directions in the area.


Item Type: Article
Email / ID Num.
O. Ameen, Ahmed
A. Alarape, Moshood
S. Adewole, Kayode
Subjects: Q Science > QA Mathematics > Mathematical statistics. Probabilities > Prediction analysis
Journal or Publication Title: Malaysian Journal of Computing (MJoC)
UiTM Journal Collections: UiTM Journal > Malaysian Journal of Computing (MJoC)
ISSN: 2600-8238
Volume: 4
Number: 2
Page Range: pp. 278-303
Keywords: Dropout prediction; educational data mining; learning analytics; machine learning
Date: December 2019
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