Predicting Student Academic Performance with Machine Learning: A Systematic Literature Review

Predicting student academic performance has become an essential research focus in higher education as institutions seek to improve retention rates, academic success, and educational quality. The increasing availability of educational datasets through student information systems and learning management systems provides opportunities for applying machine learning techniques to predict academic outcomes and identify at-risk students.
This study presents a systematic literature review (SLR) of machine learning approaches used for predicting student performance in higher education. The review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to ensure transparency and replicability.
Peer-reviewed studies published between 2015 and 2025 were collected from major academic databases including Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and Google Scholar. The screening process resulted in a final selection of relevant studies examining predictive models in educational data mining and learning analytics.
The results indicate that Random Forest, Support Vector Machines (SVM), Decision Trees, Logistic Regression, and Artificial Neural Networks are the most frequently used algorithms for student performance prediction. Several studies demonstrate predictive accuracy ranging between 70% and 95%, indicating the effectiveness of machine learning models for identifying students at risk of academic failure.
The most influential predictive features include previous academic performance, attendance records, LMS engagement, assignment submissions, and demographic characteristics. The review also identifies several research gaps, including limited use of explainable artificial intelligence, insufficient cross-institution datasets, ethical concerns related to student data, and underutilization of deep learning methods.
The findings highlight the importance of integrating predictive analytics into educational decision-making systems and developing interpretable models that support early intervention strategies in higher education.

Student Academic Performance Prediction, Machine Learning in Education, Educational Data Mining, Learning Analytics, SLR

1. D. Alboaneen, M. Almelihi, R. AlSubaie, R. Alghamdi, L. Alshehri, and R. Alharthi, “Development of a Web-Based Prediction System for Students’ Academic Performance,” Data, vol. 7, no. 2, p. 21, 2022, doi: 10.3390/data7020021. [Google Scholar] [Crossref]

2. E. J. Anagu and R. Wella, “Web-Based Machine Learning Model for Predicting Student Academic Performance in Tertiary Institutions,” J. Adv. Comput. Technol. Appl., vol. 7, no. 1, pp. 1–10, 2025. [Google Scholar] [Crossref]

3. R. S. Baker and K. Yacef, “The State of Educational Data Mining in 2009: A Review and Future Visions,” J. Educ. Data Min., vol. 1, no. 1, pp. 3–17, 2009. [Google Scholar] [Crossref]

4. P. Cortez and A. Silva, “Using Data Mining to Predict Secondary School Student Performance,” in Proceedings of the 5th Future Business Technology Conference, 2008, pp. 5–12. [Google Scholar] [Crossref]

5. R. Ferguson, “Learning Analytics: Drivers, Developments and Challenges,” Int. J. Technol. Enhanc. Learn., vol. 4, no. 5--6, pp. 304–317, 2012. [Google Scholar] [Crossref]

6. Ş. Kocakoyun-Aydoğan, T. Pura, and F. Bingül, “Predicting Students’ Academic Performances Using Machine Learning Algorithms in Educational Data Mining,” Malaysian Online J. Educ. Technol., vol. 12, no. 4, pp. 45–60, 2024. [Google Scholar] [Crossref]

7. D. Khairy, N. Alharbi, M. A. Amasha, M. F. Areed, S. Alkhalaf, and R. A. Abougalala, “Prediction of Student Exam Performance Using Data Mining Classification Algorithms,” Educ. Inf. Technol., vol. 29, pp. 21621–21645, 2024, doi: 10.1007/s10639-024-12619-w. [Google Scholar] [Crossref]

8. A. Nabil, M. Seyam, and A. Abou-elfetouh, “Prediction of Students’ Academic Performance Based on Courses’ Grades Using Deep Neural Networks,” IEEE Access, vol. 9, pp. 140731–140746, 2021, doi: 10.1109/ACCESS.2021.3119596. [Google Scholar] [Crossref]

9. C. Romero and S. Ventura, “Educational Data Mining: A Review of the State of the Art,” IEEE Trans. Syst. Man. Cybern., vol. 40, no. 6, pp. 601–618, 2010. [Google Scholar] [Crossref]

10. G. Siemens and R. Baker, “Learning Analytics and Educational Data Mining: Towards Communication and Collaboration,” in Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 2012, pp. 252–254. [Google Scholar] [Crossref]

11. M. T. Sathe and A. Adamuthe, “Comparative Study of Supervised Algorithms for Prediction of Students’ Performance,” Int. J. Mod. Educ. Comput. Sci., vol. 13, no. 1, pp. 1–12, 2021. [Google Scholar] [Crossref]

12. . Jacob and R. Henriques, “Educational Data Mining to Predict Bachelors Students’ Success,” Emerg. Sci. J., vol. 7, no. 2, pp. 345–357, 2023. [Google Scholar] [Crossref]

13. G. G. Dongre, “Predicting Student Dropout Rates in Higher Education: A Comparative Study of Machine Learning Algorithms,” Int. J. Sci. Res. Eng. Manag., vol. 8, no. 2, pp. 1–10, 2024. [Google Scholar] [Crossref]

14. F. Adamu-Fika, D. B. Madaki, A. E. Baba-Onoja, A. T. Ramalan, A. T. Mohammed, and K. S. Bature, “Modelled Machine Learning Algorithms to Predict Students’ Academic Performance in Tertiary Institutions,” in Advances in Multidisciplinary Scientific Research Conference Proceedings, 2023, pp. 418–427. [Google Scholar] [Crossref]

15. Namraiza, K. Abid, N. Aslam, M. Fuzail, M. S. Maqbool, and K. Sajid, “An Efficient Deep Learning Approach for Prediction of Student Performance Using Neural Networks,” VFAST Trans. Softw. Eng., vol. 11, no. 4, pp. 45–56, 2023. [Google Scholar] [Crossref]

16. L. Vives et al., “Prediction of Students’ Academic Performance in the Programming Fundamentals Course Using Long Short-Term Memory Neural Networks,” IEEE Access, vol. 12, pp. 5882–5898, 2024, doi: 10.1109/ACCESS.2024.3350169. [Google Scholar] [Crossref]

17. M. Yağcı, “Educational Data Mining: Prediction of Students’ Academic Performance Using Machine Learning Algorithms,” Smart Learn. Environ., vol. 9, p. 11, 2022, doi: 10.1186/s40561-022-00192-z. [Google Scholar] [Crossref]

18. D. T. Tempelaar, B. Rienties, and B. Giesbers, “In Search for the Most Informative Data for Feedback Generation: Learning Analytics in a Data-Rich Context,” Comput. Human Behav., vol. 47, pp. 157–167, 2015. [Google Scholar] [Crossref]

19. M. Z. Awang Chek and I. L. Ismail, “Maximizing Retirement Savings: Strategic Forecasting of Employees’ Provident Fund (EPF) Dividends,” Int. J. Res. Innov. Soc. Sci., 2024. [Google Scholar] [Crossref]

20. M. Syakir, M. Z. A. Chek, and I. L. Ismail, “Understanding A Long-Term Care towards Ageing Population in Malaysia,” Int. J. Acad. Res. Bus. Soc. Sci., vol. 13, no. 12, pp. 4744–4754, 2023, doi: 10.6007/ijarbss/v13-i12/20328. [Google Scholar] [Crossref]

21. I. L. Ismail, N. F. Jamal, M. Z. Awang Chek, and M. S. Baharuddin, “Learning Basic Statistics and Probability Through MOOC,” Int. J. Mod. Trends Soc. Sci., vol. 2, no. 8, pp. 99–107, 2019, doi: 10.35631/ijmtss.280010. [Google Scholar] [Crossref]

22. A. N. A. Ahmad Ridzuan, M. Z. Awang Chek, N. M. Abdul Ghafar, and A. B. Ahmad, “Developing an Introduction to Actuarial Science MOOC,” Int. J. Acad. Res. Bus. Soc. Sci., vol. 8, no. 1, pp. 600–605, 2018, doi: 10.6007/ijarbss/v8-i1/3833. [Google Scholar] [Crossref]

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