ARCHIVES
Original Article
Predicting Student Academic Performance Based on Previous Academic Records: A Machine Learning Approach
Charuta Khadke1
Bhagyashree Nishane2
Dr. Yogesh N. Chaudhari3
1 2 3 Assistant Professor, KCES’s Institute of Management And Research, Jalgaon, Maharashtra, India.
Published Online: May-June 2026
Pages: 405-413
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260703044References
1. Arifin, M., Widowati, W., Farikhin, F., & Gudnanto, G. (2023). A regression model and a combination of academic and non-academic
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arXiv:1606.06364.
3. Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational
Data Mining, 1(1), 3–17.4. Batool, S., Rashid, J., Nisar, M. W., Kim, J., Kwon, H.-Y., & Hussain, A. (2023). Educational data mining to predict students' academic
performance: A survey study. Education and Information Technologies, 28(1), 905–971.
5. Cortez, P., & Silva, A. M. G. (2008). Using data mining to predict secondary school student performance. In A. Brito & J. Teixeira
(Eds.), Proceedings of the 5th Annual Future Business Technology Conference (FUBUTEC 2008) (pp. 5–12). EUROSIS.
6. Del Bonifro, F., Gabbrielli, M., Lisanti, G., & Zingaro, S. P. (2020). Student dropout prediction. In Artificial Intelligence in Education:
21st International Conference, AIED 2020, Proceedings, Part I (pp. 129–140). Springer.
7. Discover Education (2025). Performance prediction using educational data mining techniques: A comparative study. Discover
Education, 4, Article 89.
8. Dol, S. M., & Jawandhiya, P. M. (2023). Classification technique and its combination with clustering and association rule mining in
educational data mining — A survey. Engineering Applications of Artificial Intelligence, 122, 106071.
9. Du, X., Yang, J., Hung, J.-L., & Shelton, B. (2020). Educational data mining: A systematic review of research and emerging trends.
Information Discovery and Delivery, 48(4), 225–236.
10. Hasib, K. M., Rahman, F., Hasnat, R., & Alam, M. G. R. (2022). A machine learning and explainable AI approach for predicting
secondary school student performance. In Proceedings of the IEEE 12th Annual Computing and Communication Workshop and
Conference (CCWC) (pp. 399–405). IEEE.
11. IEEE (2024). Student academic performance prediction using machine learning with various features and scenarios. IEEE Conference
Publication.
12. IEEE (2025). Student performance prediction system using machine learning. IEEE Conference Publication.
13. Knowles, J. E. (2015). Of needles and haystacks: Building an accurate statewide dropout early warning system in Wisconsin. Journal
of Educational Data Mining, 7(3), 18–67.
14. MDPI Information (2026). Machine learning and deep learning for dropout prediction in higher education: A review. Information,
15(3), Article 164.
15. Ozyurt, O., Ozyurt, H., & Mishra, D. (2023). Uncovering the educational data mining landscape and future perspective: A
comprehensive analysis. IEEE Access, 11, 120192–120208.
16. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V.,
Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python.
Journal of Machine Learning Research, 12, 2825–2830.
17. Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1),
135–146.
18. Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and
Cybernetics, Part C (Applications and Reviews), 40(6), 601–618.
19. Romero, C., & Ventura, S. (2013). Data mining in education. WIREs Data Mining and Knowledge Discovery, 3(1), 12–27.
20. Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and
Knowledge Discovery, 10(3), e1355.
21. ScholarWorks UTRGV (2025). Predicting students' academic performance via machine learning models: An empirical review and
practical application. University of Texas Rio Grande Valley, Faculty Publications and Presentations.
22. Wang, J., & Yu, Y. (2025). Machine learning approach to student performance prediction of online learning. PLOS ONE, 20(1),
e0299018.
23. Xing, W., & Du, D. (2019). Dropout prediction in MOOCs: Using deep learning for personalized intervention. Journal of Educational
Computing Research, 57(3), 547–570.
24. arXiv (2025). Evaluation of machine learning models in student academic performance prediction. arXiv preprint, arXiv:2506.08047.
25. arXiv (2025). Machine learning-driven student performance prediction for enhancing tiered instruction. arXiv preprint,
arXiv:2502.03143
features to predict student academic performance. TEM Journal, 12(2), 855–864.
2. Aulck, L., Velagapudi, N., Blumenstock, J., & West, J. (2016). Predicting student dropout in higher education. arXiv preprint,
arXiv:1606.06364.
3. Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational
Data Mining, 1(1), 3–17.4. Batool, S., Rashid, J., Nisar, M. W., Kim, J., Kwon, H.-Y., & Hussain, A. (2023). Educational data mining to predict students' academic
performance: A survey study. Education and Information Technologies, 28(1), 905–971.
5. Cortez, P., & Silva, A. M. G. (2008). Using data mining to predict secondary school student performance. In A. Brito & J. Teixeira
(Eds.), Proceedings of the 5th Annual Future Business Technology Conference (FUBUTEC 2008) (pp. 5–12). EUROSIS.
6. Del Bonifro, F., Gabbrielli, M., Lisanti, G., & Zingaro, S. P. (2020). Student dropout prediction. In Artificial Intelligence in Education:
21st International Conference, AIED 2020, Proceedings, Part I (pp. 129–140). Springer.
7. Discover Education (2025). Performance prediction using educational data mining techniques: A comparative study. Discover
Education, 4, Article 89.
8. Dol, S. M., & Jawandhiya, P. M. (2023). Classification technique and its combination with clustering and association rule mining in
educational data mining — A survey. Engineering Applications of Artificial Intelligence, 122, 106071.
9. Du, X., Yang, J., Hung, J.-L., & Shelton, B. (2020). Educational data mining: A systematic review of research and emerging trends.
Information Discovery and Delivery, 48(4), 225–236.
10. Hasib, K. M., Rahman, F., Hasnat, R., & Alam, M. G. R. (2022). A machine learning and explainable AI approach for predicting
secondary school student performance. In Proceedings of the IEEE 12th Annual Computing and Communication Workshop and
Conference (CCWC) (pp. 399–405). IEEE.
11. IEEE (2024). Student academic performance prediction using machine learning with various features and scenarios. IEEE Conference
Publication.
12. IEEE (2025). Student performance prediction system using machine learning. IEEE Conference Publication.
13. Knowles, J. E. (2015). Of needles and haystacks: Building an accurate statewide dropout early warning system in Wisconsin. Journal
of Educational Data Mining, 7(3), 18–67.
14. MDPI Information (2026). Machine learning and deep learning for dropout prediction in higher education: A review. Information,
15(3), Article 164.
15. Ozyurt, O., Ozyurt, H., & Mishra, D. (2023). Uncovering the educational data mining landscape and future perspective: A
comprehensive analysis. IEEE Access, 11, 120192–120208.
16. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V.,
Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python.
Journal of Machine Learning Research, 12, 2825–2830.
17. Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1),
135–146.
18. Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and
Cybernetics, Part C (Applications and Reviews), 40(6), 601–618.
19. Romero, C., & Ventura, S. (2013). Data mining in education. WIREs Data Mining and Knowledge Discovery, 3(1), 12–27.
20. Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and
Knowledge Discovery, 10(3), e1355.
21. ScholarWorks UTRGV (2025). Predicting students' academic performance via machine learning models: An empirical review and
practical application. University of Texas Rio Grande Valley, Faculty Publications and Presentations.
22. Wang, J., & Yu, Y. (2025). Machine learning approach to student performance prediction of online learning. PLOS ONE, 20(1),
e0299018.
23. Xing, W., & Du, D. (2019). Dropout prediction in MOOCs: Using deep learning for personalized intervention. Journal of Educational
Computing Research, 57(3), 547–570.
24. arXiv (2025). Evaluation of machine learning models in student academic performance prediction. arXiv preprint, arXiv:2506.08047.
25. arXiv (2025). Machine learning-driven student performance prediction for enhancing tiered instruction. arXiv preprint,
arXiv:2502.03143
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