Educational data mining, student academic performance prediction, prediction methods, algorithms and tools: an overview of reviews

• Published in: Je-LKS Vol. 18; no. 2
• Main Author: Chaka Chaka
• Format: Journal Article
• Language: English
• Published: Italian e-Learning Association 01.08.2022
• Subjects: Algorithms; Educational Data Mining; Methods; Overview; Student Academic Performance
• ISSN: 1826-6223; 1971-8829
• DOI: 10.20368/1971-8829/1135578

This overview study set out to compare and synthesise the findings of review studies conducted on predicting student academic performance (SAP) in higher education using educational data mining (EDM) methods, EDM algorithms, and EDM tools from 2013 to September 2021. It conducted multiple searches for suitable and relevant peer-reviewed articles on two online search engines, on nine online databases, and on two online academic social networks. It, then, selected 33 eligible articles from 2,500 articles. Some of the findings of this overview study are worth mentioning. First, only 3 studies explicitly stated their precise sample sizes, and only 5 studies explicitly mentioned their subject areas with maths and science, and computer science and engineering as the four most mentioned subject areas. Second, 20 review studies had purposes related to either EDM techniques, EDM methods, EDM models, or EDM algorithms employed to predict SAP and student success in the higher education sector. Third, there are six commonly used typologies of input variables reported by 33 review studies, of which student demographics was the most commonly utilised variable for predicting SAP. Fourth and last, seven common EDM algorithms employed for predicting SAP were identified, of which Decision Tree emerged both as the most used algorithm and as the algorithm with the highest prediction accuracy rate for predicting SAP.