Authors :
Gabriel Adaugo Cynthia; Prema Kirubakaran; Ridwan Kolapo
Volume/Issue :
Volume 11 - 2026, Issue 9 - September
Google Scholar :
https://tinyurl.com/ywmjww27
DOI :
https://doi.org/10.38124/ijisrt/26sep648
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
Early detection of students who are likely to have low performance levels is a major issue in learning institutions. This paper aims at creating a predictive model that can be depended on as well as interpreted to deal with this problem using machine learning. Another strength of this research is that it focuses on accuracy, among other factors, which is unlike many other studies that focus on accuracy only but not on model robustness and contextual relevance, which are essential to meaningful educational decision-making. To enhance the credibility of the findings, the primary and secondary data were also used, including demographic, socio-educational, and academic characteristics of students.
Keywords :
Student Academic Performance, Machine Learning, Educational Data Mining, Learning Analytics.
References :
- Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355. https://doi.org/10.1002/widm.1355
- Lampropoulos, G. (2023). Educational data mining and learning analytics in the 21st century. In Encyclopedia of data science and machine learning (pp. 1642–1651). IGI Global.
- Christou, V., Tsoulos, I., Loupas, V., Tzallas, A. T., Gogos, C., Karvelis, P. S., et al. (2023). Performance and early drop prediction for higher education students using machine learning. Expert Systems with Applications, 225, 120079. https://doi.org/10.1016/j.eswa.2023.120079
- Albreiki, B., Zaki, N., & Alashwal, H. (2021). A systematic literature review of student performance prediction using machine learning techniques. Education Sciences, 11(9), 552. https://doi.org/10.3390/educsci11090552
Early detection of students who are likely to have low performance levels is a major issue in learning institutions. This paper aims at creating a predictive model that can be depended on as well as interpreted to deal with this problem using machine learning. Another strength of this research is that it focuses on accuracy, among other factors, which is unlike many other studies that focus on accuracy only but not on model robustness and contextual relevance, which are essential to meaningful educational decision-making. To enhance the credibility of the findings, the primary and secondary data were also used, including demographic, socio-educational, and academic characteristics of students.
Keywords :
Student Academic Performance, Machine Learning, Educational Data Mining, Learning Analytics.
