Learning Analytics and Predictive Modeling: Enhancing Student Success through Data-Driven Insights
DOI:
https://doi.org/10.70882/josrar.2025.v2i3.77Keywords:
Academic Performance Prediction, Explainable AI (SHAP), Learning Analytics, Predictive Modeling, Student EngagementAbstract
In the evolving landscape of data-informed education, predictive modeling has become a powerful tool for identifying students at risk of academic failure or withdrawal. This study investigates the use of learning analytics techniques to predict student outcomes using the Open University Learning Analytics Dataset (OULAD), a comprehensive, publicly available dataset that includes demographic profiles, continuous assessment records, and detailed interaction logs from a virtual learning environment (VLE). By integrating and preprocessing these data sources, the authors developed a comprehensive set of behavioral and temporal features, with particular focus on total click activity, which acts as a proxy for student engagement. The prediction task was framed as a binary classification problem: distinguishing students who completed a course (pass or distinction) from those who failed or withdrew. Although the specific classification algorithm is not explicitly identified, the trained model achieved a classification accuracy of 71% and an area under the receiver operating characteristic curve (ROC-AUC) of 0.79, indicating a reasonably high level of discriminative ability. A key strength of the study is its use of SHAP (Shapley Additive Explanations) values to interpret the model’s output, offering transparency into how individual features influenced prediction results. The analysis showed that engagement-related features, especially VLE click counts, had the greatest predictive power, while demographic variables such as gender and age contributed little, suggesting a reduced risk of bias from protected attributes. These findings underscore the practical value of interpretable predictive models in supporting early warning systems and learner support strategies in higher education. Additionally, the study addresses important ethical considerations by emphasizing fairness, privacy, and the need for explainable AI. While some methodological limitations remain, such as the lack of algorithm disclosure and validation details, the research provides valuable insights into designing transparent, ethical, and actionable learning analytics tools.
References
Afzaal, M., Nouri, J., Zia, A., Papapetrou, P., Fors, U., Wu, Y., Li, X., & Weegar, R. (2021). Generation ofAutomatic Data-Driven Feedback to Students Using Explainable Machine Learning. In I. Roll, D. McNamara, S. Sosnovsky, R. Luckin, & V. Dimitrova (Eds.), Artificial Intelligence in Education (Vol. 12749, pp. 37–42). Springer International Publishing. https://doi.org/10.1007/978-3-030-78270-2_6
Ahn, J., Nguyen, H., Campos, F., & Young, W. (2021). Transforming Everyday Information into Practical Analytics with Crowdsourced Assessment Tasks. LAK21: 11th International Learning Analytics and Knowledge Conference, 66–76. https://doi.org/10.1145/3448139.3448146
Albreiki, B., Habuza, T., & Zaki, N. (2022). Framework for automatically suggesting remedial actions to help students at risk based on explainable ML and rule-based models. International Journal of Educational Technology in Higher Education, 19(1), 49. https://doi.org/10.1186/s41239-022-00354-6
Capuano, N., Rossi, D., Ströele, V., & Caballé, S. (2023a). Explainable Prediction of Student Performance in Online Courses. In D. Guralnick, M. E. Auer, & A. Poce (Eds.), Creative Approaches to Technology-Enhanced Learning for the Workplace and Higher Education (Vol. 767, pp. 639–652). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-41637-8_52
Capuano, N., Rossi, D., Ströele, V., & Caballé, S. (2023b). Explainable Prediction of Student Performance in Online Courses. In D. Guralnick, M. E. Auer, & A. Poce (Eds.), Creative Approaches to Technology-Enhanced Learning for the Workplace and Higher Education (Vol. 767, pp. 639–652). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-41637-8_52
Carpenter, D., Cloude, E., Rowe, J., Azevedo, R., & Lester, J. (2021). Investigating Student Reflection during Game-Based Learning in Middle Grades Science. LAK21: 11th International Learning Analytics and Knowledge Conference, 280–291. https://doi.org/10.1145/3448139.3448166
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22. https://doi.org/10.1186/s41239-023-00392-8
Fahd, K., Venkatraman, S., Miah, S. J., & Ahmed, K. (2022). Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature. Education and Information Technologies, 27(3), 3743–3775. https://doi.org/10.1007/s10639-021-10741-7
Hwang, G.-J., & Tu, Y.-F. (2021). Roles and Research Trends of Artificial Intelligence in Mathematics Education: A Bibliometric Mapping Analysis and Systematic Review. Mathematics, 9(6), 584. https://doi.org/10.3390/math9060584
James, G. G., P, O. G., G, C. E., A, M. N., F, E. W., & E, O. P. (2024). Optimizing Business Intelligence System Using Big Data and Machine Learning. Journal of Information Systems and Informatics, 6(2), 1215–1236. https://doi.org/10.51519/journalisi.v6i2.631
Ko, G. Y., Shin, D., Auh, S., Lee, Y., & Han, S. P. (2023). Learning Outside the Classroom During a Pandemic: Evidence from an Artificial Intelligence-Based Education App. Management Science, 69(6), 3616–3649. https://doi.org/10.1287/mnsc.2022.4531
Liang, Z., Sha, L., Tsai, Y.-S., Gašević, D., & Chen, G. (2024). Towards the Automated Generation of Readily Applicable Personalised Feedback in Education. In A. M. Olney, I.-A. Chounta, Z. Liu, O. C. Santos, & I. I. Bittencourt (Eds.), Artificial Intelligence in Education (Vol. 14830, pp. 75–88). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64299-9_6
Motz, B. A., Bergner, Y., Brooks, C. A., Gladden, A., Gray, G., Lang, C., Li, W., Marmolejo-Ramos, F., & Quick, J. D. (2023). LAK of Direction: Misalignment Between the Goals of Learning Analytics and its Research Scholarship. Journal of Learning Analytics, 1–13. https://doi.org/10.18608/jla.2023.7913
Oise, G., & Konyeha, S. (2024). E-WASTE MANAGEMENT THROUGH DEEP LEARNING: A SEQUENTIAL NEURAL NETWORK APPROACH. FUDMA JOURNAL OF SCIENCES, 8(3), 17–24. https://doi.org/10.33003/fjs-2024-0804-2579
Oise, G. P., & Akpowehbve, O. J. (2024). Systematic Literature Review on Machine Learning Deep Learning and IOT-based Model for E-Waste Management. International Transactions on Electrical Engineering and Computer Science, 3(3), 154–162. https://doi.org/10.62760/iteecs.3.3.2024.94
Oise, G. P., & Konyeha, S. (2024). Deep Learning System for E-Waste Management. The 3rd International Electronic Conference on Processes, 66. https://doi.org/10.3390/engproc2024067066
Oise, G. P., Nwabuokei, O. C., Akpowehbve, O. J., Eyitemi, B. A., & Unuigbokhai, N. B. (2025). TOWARDS SMARTER CYBER DEFENSE: LEVERAGING DEEP LEARNING FOR THREAT IDENTIFICATION AND PREVENTION. FUDMA JOURNAL OF SCIENCES, 9(3), 122–128. https://doi.org/10.33003/fjs-2025-0903-3264
Onyema, E. M., Almuzaini, K. K., Onu, F. U., Verma, D., Gregory, U. S., Puttaramaiah, M., & Afriyie, R. K. (2022). Prospects and Challenges of Using Machine Learning for Academic Forecasting. Computational Intelligence and Neuroscience, 2022, 1–7. https://doi.org/10.1155/2022/5624475
Rashidian, N., & Hilal, M. A. (2022). Applications of machine learning in surgery: Ethical considerations. Artificial Intelligence Surgery. https://doi.org/10.20517/ais.2021.13
Sassirekha, M. S., & Vijayalakshmi, S. (2022). Predicting the academic progression in student’s standpoint using machine learning. Automatika, 63(4), 605–617. https://doi.org/10.1080/00051144.2022.2060652
Simaei, E., & Rahimifard, S. (2024). AI-based decision support system for enhancing end-of-life value recovery from e-wastes. International Journal of Sustainable Engineering, 17(1), 1–17. https://doi.org/10.1080/19397038.2024.2306293
Susnjak, T. (2024). Beyond Predictive Learning Analytics Modelling and onto Explainable Artificial Intelligence with Prescriptive Analytics and ChatGPT. International Journal of Artificial Intelligence in Education, 34(2), 452–482. https://doi.org/10.1007/s40593-023-00336-3
Umer, R., Susnjak, T., Mathrani, A., & Suriadi, L. (2023). Current stance on predictive analytics in higher education: Opportunities, challenges and future directions. Interactive Learning Environments, 31(6), 3503–3528. https://doi.org/10.1080/10494820.2021.1933542
Valdiviezo-Diaz, P., & Chicaiza, J. (2024). Prediction of Academic Outcomes Using Machine Learning Techniques: A Survey of Findings on Higher Education. In M. Botto-Tobar, M. Zambrano Vizuete, S. Montes León, P. Torres-Carrión, & B. Durakovic (Eds.), International Conference on Applied Technologies (Vol. 2049, pp. 206–218). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-58956-0_16
Waheed, H., Hassan, S.-U., Nawaz, R., Aljohani, N. R., Chen, G., & Gasevic, D. (2023a). Early prediction of learners at risk in self-paced education: A neural network approach. Expert Systems with Applications, 213, 118868. https://doi.org/10.1016/j.eswa.2022.118868
Waheed, H., Hassan, S.-U., Nawaz, R., Aljohani, N. R., Chen, G., & Gasevic, D. (2023b). Early prediction of learners at risk in self-paced education: A neural network approach. Expert Systems with Applications, 213, 118868. https://doi.org/10.1016/j.eswa.2022.118868
Zhang, Z., & Xu, L. (2024). Student engagement with automated feedback on academic writing: A study on Uyghur ethnic minority students in China. Journal of Multilingual and Multicultural Development, 45(8), 3466–3479. https://doi.org/10.1080/01434632.2022.2102175
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Science Research and Reviews
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial — You may not use the material for commercial purposes.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
