In this paper, we explore the application of predictive modeling within the field of Learning Analytics (LA) to forecast student academic success in higher education. Utilizing the Open University Learning Analytics Dataset (OULAD), we integrate student demographic, educational, and assessment data to build a dataset suitable for supervised learning. Two models are employed: logistic regression, chosen for its interpretability, and Random Forest, selected for its capacity to capture complex, non-linear relationships. Our target variable is whether a student passes a course module. The analysis reveals that performance in early assessments is the most influential predictor, followed by prior education level and age group. The Random Forest model consistently outperforms logistic regression across all performance metrics, including accuracy, precision, and recall. These results emphasize the potential of machine learning to support early identification of at-risk students, guiding timely interventions. We conclude by discussing the policy implications of our findings for institutional strategies aimed at improving student retention and academic outcomes.
Alhammad, A. I. (2023). Learning Analytics in the EFL Classroom: Technology as a Forecasting Tool. Research Journal in Advanced Humanities, 4, 97-108. https://doi.org/10.58256/rjah.v4i3.1194
Arnold, K. E., & Pistilli, M. D. (2012). Course Signals at Purdue. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 267-270). ACM. https://doi.org/10.1145/2330601.2330666
Baker, R. S. J., & Inventado, P. S. (2014). Chapter X: Educational Data Mining and Learning Analytics. Computer Science, 7, 1-16.
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, 3-17.
Bañeres, D., Rodríguez, M. E., Guerrero-Roldán, A. E., & Karadeniz, A. (2020). An Early Warning System to Detect At-Risk Students in Online Higher Education. Applied Sciences, 10, Article 4427. https://doi.org/10.3390/app10134427
Beck, H. P., & Davidson, W. D. (2001). Establishing an Early Warning System: Predicting Low Grades in College Students from Survey of Academic Orientations Scores. Research in Higher Education, 42, 709-723. https://doi.org/10.1023/a:1012253527960
Bousbia, N., & Belamri, I. (2014). Which Contribution Does EDM Provide to Computer-Based Learning Environments? In Studies in Computational Intelligence (pp. 3-28). Springer. https://doi.org/10.1007/978-3-319-02738-8_1
Gaˇsevi´c, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning Analytics Should Not Promote One Size Fits All: The Effects of Instructional Conditions in Predicting Academic Success. The Internet and Higher Education, 28, 68-84. https://doi.org/10.1016/j.iheduc.2015.10.002
Giacumo, L. A., & Bremen, J. (2016). Emerging Evidence on the use of Big Data and Analytics in Workplace Learning: A Systematic Literature Review. Quarterly Review of Distance Education, 17, 21-38.
Gourna, S., Rigou, A., Kyriazi, F., & Marinagi, C. (2024). The Added Value of Learning Analytics in Higher Education. International Journal of Education and Information Technologies, 18, 133-142. https://doi.org/10.46300/9109.2024.18.13
Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Journal of Educational Technology and Society, 15, 42-57.
Guerard, J., Thomakos, D., Kyriazi, F., & Mamais, K. (2023). On the Predictability of the DJIA and S&P500 Indices. Wilmott Magazine , 129, 1-84.
Gupta, O. J., & Yadav, S. (2023). Determinants in Advancement of Teaching and Learning in Higher Education: In Special Reference to Management Education. The International Journal of Management Education, 21, Article 100823. https://doi.org/10.1016/j.ijme.2023.100823
Kotsiantis, S., Tselios, N., Filippidi, A., & Komis, V. (2013). Using Learning Analytics to Identify Successful Learners in a Blended Learning Course. International Journal of Technology Enhanced Learning, 5, 133-150. https://doi.org/10.1504/ijtel.2013.059088
Krumm, A. E., Waddington, R. J., Teasley, S. D., & Lonn, S. (2014). A Learning Management System-Based Early Warning System for Academic Advising in Undergraduate Engineering. In Learning Analytics (pp. 103-119). Springer. https://doi.org/10.1007/978-1-4614-3305-7_6
Kulik, C. L. C., Kulik, J. A., & Bangert-Drowns, R. L. (1990). Effectiveness of Mastery Learning Programs: A Meta-Analysis. Review of Educational Research, 60, 265-299. https://doi.org/10.3102/00346543060002265
Kyriazi, F. (2024). The Prescriptive Nature of Market Timing and Predictive Portfolios. IMA Journal of Management Mathematics, 36, 323-338. https://doi.org/10.1093/imaman/dpae027
Libbrecht, P., Rebholz, S., Herding, D., Müller, W., & Tscheulin, F. (2012). Understanding the Learners’ Actions When Using Mathematics Learning Tools. In Lecture Notes in Computer Science (pp. 111-126). Springer. https://doi.org/10.1007/978-3-642-31374-5_8
Long, P. D., & Siemens, G. (2014). Penetrating the Fog: Analytics in Learning and Education. Tecnologie Didattiche, 22, 132-137. https://doi.org/10.17471/2499-4324/195
Mubarak, A. A., Cao, H., & Ahmed, S. A. M. (2020). Predictive Learning Analytics Using Deep Learning Model in MOOCs’ Courses Videos. Education and Information Technologies, 26, 371-392. https://doi.org/10.1007/s10639-020-10273-6
Nguyen, A., Gardner, L., & Sheridan, D. (2020). Data Analytics in Higher Education: An Integrated View. Journal of Information Systems Education , 31, 61-71. https://aisel.aisnet.org/jise/vol31/iss1/5
Papamitsiou, Z., & Economides, A. A. (2014). Temporal Learning Analytics for Adaptive Assessment. Journal of Learning Analytics, 1, 165-168. https://doi.org/10.18608/jla.2014.13.13
Rincon-Flores, E. G., Lopez-Camacho, E., Mena, J., & Olmos, O. (2022). Teaching through Learning Analytics: Predicting Student Learning Profiles in a Physics Course at a Higher Education Institution. International Journal of Interactive Multimedia and Artificial Intelligence, 7, 82-89. https://doi.org/10.9781/ijimai.2022.01.005
Rogers, T., Gašević, D., & Dawson, S. (2016). Learning Analytics and the Imperative for Theory-Driven Research. The SAGE Handbook of E-learning Research, 232-250. https://doi.org/10.4135/9781473955011.n12
Siemens, G. (2019). Learning Analytics and Open, Flexible, and Distance Learning. Distance Education, 40, 414-418. https://doi.org/10.1080/01587919.2019.1656153
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The Current Landscape of Learning Analytics in Higher Education. Computers in Human Behavior, 89, 98-110. https://doi.org/10.1016/j.chb.2018.07.027
Wang, Y. (2016). Big Opportunities and Big Concerns of Big Data in Education. TechTrends, 60, 381-384. https://doi.org/10.1007/s11528-016-0072-1