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Review Article

A Concept Drift Adaptive Machine Learning Model for Predicting Student Academic Performance Using Learning Management System

Open Access Peer Reviewed Crossref DOI

Authors

  • Martin Ngure1
  • Cynthia Ikamari2
  • Anthony Mile3

1 3 Department of Computer Science and Information Technology, School of Computing and Mathematics, Co-operative University of Kenya, Kenya. 2 Department of Mathematics, School of Computing and Mathematics, Co-operative University of Kenya, Kenya.

Article Information

Published Online
September-December 2026
Pages
606-613
Article Type
Review Article
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References

1. Dake, D. K., & Buabeng-Andoh, C. Using Machine Learning Techniques to Predict Learner Drop-out Rate in Higher Educational Institutions. Mobile Information Systems. 2022; 2022: 1-9. https://doi.org/10.1155/2022/2670562
2. Ogwoka, T. M., Oboko, R. O., & Chepken, C. K. Comparative Analysis of Machine Learning Classifier Models for Predicting Student Cognitive Load and Performance Outcomes in Moodle Learning Environment. East African Journal of Information Technology. 2024; 7(1): 301-317. https://doi.org/10.37284/eajit.7.1.2227
3. Azizah, Z., Ohyama, T., Zhao, X., Ohkawa, Y., & Mitsuishi, T. Predicting at-risk students in the early stage of a blended learning course via machine learning using limited data. Computers and Education: Artificial Intelligence. 2024; 7: 100261. https://doi.org/10.1016/j.caeai.2024.100261
4. Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. A survey on concept drift adaptation. ACM Computing Surveys. 2014; 46(4): 1-37. https://doi.org/10.1145/2523813
5. Webb, G. I., Hyde, R., Cao, H., Nguyen, H. L., & Petitjean, F. Characterizing concept drift. Data Mining and Knowledge Discovery. 2016; 30(4): 964-994. https://doi.org/10.1007/s10618-015-0448-4
6. Sonnleitner, B., Madou, T., Deceuninck, M., Theodosiou, F., & Sagaert, Y. R. Evaluation of early student performance prediction given concept drift. Computers and Education: Artificial Intelligence. 2025; 8: 100369. https://doi.org/10.1016/j.caeai.2025.100369
7. Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., & Zhang, G. Learning under Concept Drift: A Review. IEEE Transactions on Knowledge and Data Engineering. 2018. https://doi.org/10.1109/TKDE.2018.2876857
8. Prinsloo, P., & Kaliisa, R. Learning Analytics on the African Continent: An Emerging Research Focus and Practice. Journal of Learning Analytics. 2022; 9(2): 218-235. https://doi.org/10.18608/jla.2022.7539
9. James, G., Witten, D., Hastie, T., & Tibshirani, R. An Introduction to Statistical Learning: With Applications in R. Springer; 2021. https://doi.org/10.1007/978-1-0716-1418-1
10. Little, R., & Rubin, D. Statistical Analysis with Missing Data. 3rd ed. Wiley; 2019. https://doi.org/10.1002/9781119482260
11. Bifet, A., & Gavalda, R. Learning from Time-Changing Data with Adaptive Windowing. Proceedings of the 2007 SIAM International Conference on Data Mining. 2007: 443-448. https://doi.org/10.1137/1.9781611972771.42
12. Goncalves, P. M., De Carvalho Santos, S. G. T., Barros, R. S. M., & Vieira, D. C. L. A comparative study on concept drift detectors.
13. Expert Systems with Applications. 2014; 41(18): 8144-8156. https://doi.org/10.1016/j.eswa.2014.07.019
14. Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M., & Siemens, G. Opportunities of artificial intelligence for supporting complex problem-solving: Findings from a scoping review. Computers and Education: Artificial Intelligence. 2023; 4: 100138. https://doi.org/10.1016/j.caeai.2023.100138
15. Creswell, J. W., & Creswell, J. D. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 5th ed. SAGE; 2018.
16. Saunders, M., Thornhill, A., & Lewis, P. Research Methods for Business Students. 8th ed. Pearson; 2019.
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