Learning analytics: predicting student performance based on student activity and performance data in e-learning course management systems

This study examines the problem of increasing the accuracy of predicting the learning outcomes of university students based on data on current academic performance in disciplines from electronic educational courses. The source data are numerical values – grades and activity indicators based on the results of each week of training – which may relate to different scale ranges and be highly sparse, making their subsequent use in forecasting algorithms difficult. Therefore, it is necessary to conduct high-quality preprocessing of the source information to construct a predictive mathematical model of learning outcomes. One proposed method for data processing is the use of percentiles, which effectively describe the distribution of features. The percentile method solves the issue of fairly comparing students enrolled in courses with differing difficulty levels by turning raw scores into relative rankings within a learner's peer group. This study presents a side-by-side comparison of two data processing techniques: the conventional centering-and-norming method versus the percentile-based strategy. A nonparametric estimator designed for the Nadaraya-Watson regression function is then employed to build the predictive model. Excerpts from computational experiments with varying numbers of groups are presented, yielding acceptable results from a practical perspective.

1. Zhang Y., Yun Y., An R., Cui J., Dai H., Shang X. Educational Data Mining Techniques for Student Performance Prediction: Method Review and Comparison Analysis. Frontiers in Psychology. 2021;12:698490. https://doi.org/10.3389/fpsyg.2021.698490

2. Angeioplastis A., Aliprantis J., Konstantakis M., Tsimpiris A. Predicting Student Performance and Enhancing Learning Outcomes: A Data-Driven Approach Using Educational Data Mining Techniques. Computers. 2025;14(3):83. https://doi.org/10.3390/computers14030083

3. Lu Ch., Cutumisu M. Online engagement and performance on formative assessments mediate the relationship between attendance and course performance. International Journal of Educational Technology in Higher Education. 2022;19(1):2. https://doi.org/10.1186/s41239-021-00307-5

4. Han L. Prediction and Analysis of Students' Behavior Based on Data Mining in Educational Administration. In: Artificial Intelligence for Future Society: Proceedings of the International Conference on Artificial Intelligence for Society, 18–19 May 2024, Bhubaneswar, India. Cham: Springer; 2024. P. 229–238. https://doi.org/10.1007/978-3-031-69457-8_22

5. Wang J., Yu Y. Machine learning approach to student performance prediction of online learning. PLoS ONE. 2025;20(1):e0299018. https://doi.org/10.1371/journal.pone.0299018

6. Al-Tameemi Gh., Xue J., Hadi Ali I., Ajit S. A hybrid machine learning approach for predicting student performance using multi-class educational datasets. Procedia Computer Science. 2024;238:888–895. https://doi.org/10.1016/j.procs.2024.06.108

7. Gadde S.S., Anand D., Sasidhar Babu N., Pujitha B.V., Sai Reethi M., Pradeep Ghantasala G.S. Performance Prediction of Students Using Machine Learning Algorithms. In: Applications of Computational Methods in Manufacturing and Product Design: Select Proceedings of IPDIMS 2020, 02–03 December 2020, Rourkela, India. Singapore: Springer; 2022. P. 405–411. https://doi.org/10.1007/978-981-19-0296-3_36

8. Duch D., May M., George S. Enhancing Predictive Analytics for Students' Performance in Moodle: Insight from an Empirical Study. Journal of Data Science and Intelligent Systems. 2026;4(1):84–96. https://doi.org/10.47852/bonviewJDSIS42023777

9. Popova N.A., Egorova E.S. Main directions of data mining in the field of education. News of the Kabardino-Balkarian Scientific Center of RAS. 2024;26(5):94–106. (In Russ.). https://doi.org/10.35330/1991-6639-2024-26-5-94-106

10. Papadogiannis I., Wallace M., Karountzou G. Educational Data Mining: A Foundational Overview. Encyclopedia. 2024;4(4):1644–1664. https://doi.org/10.3390/encyclopedia4040108

11. Lin Y., Chen H., Xia W., Lin F., Wang Z., Liu Y. A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining. Data Science and Engineering. 2025;10(4):564–590. https://doi.org/10.1007/s41019-025-00303-z

12. Yakunin Yu.Yu., Shestakov V.N., Liksonova D.I., Danichev A.A. Predicting student performance using machine learning tools. Informatics and education. 2023;38(4):28–43. (In Russ.). https://doi.org/10.32517/0234-0453-2023-38-4-28-43

13. Liksonova D.I., Danichev A.A., Shestakov V.N., Yakunin Yu.Yu. Increasing the accuracy of the model for predicting the performance of university students. Information Technologies. 2025;31(4):215–224. (In Russ.). https://doi.org/10.17587/it.31.215-224

14. Nadaraya E.A. Nonparametric estimation of probability density and regression curve. Tbilisi: Izd-vo TGU; 1983. 194 p. (In Russ.).