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Application of Artificial Intelligence Techniques for Predicting Academic Performance in Mathematics Courses

10.24050/reia.v23i45.1933
Published 2026-01-01
Articles

Abstract

Low academic performance in mathematics courses in higher education is a recurring cause of failure and student dropout, particularly in science, technology, and engineering programs. This article presents the development of a predictive model based on machine learning techniques, aimed at early identification of students at risk of failing such courses. An institutional dataset comprising information from 2,932 students admitted to Universidad del Valle between 2021 and 2023 was used. The dataset includes demographic, socioeconomic, and academic variables, as well as the results of a mathematics diagnostic test. Neural networks, decision trees, and support vector machines were trained and evaluated using preprocessing, class balancing, and cross-validation techniques. The neural network model achieved the best performance, with an AUC of 70.6%, accuracy of 64.9%, sensitivity of 64.4%, and specificity of 65.1%, outperforming decision tree models (AUC of 46.7%) and SVMs (AUC of 66.1%). Compared to previous studies, the results are competitive given the heterogeneity of the data and the model's practical integration. Unlike other approaches that rely on data generated during the semester, this model operates solely with information available at the time of admission, enabling timely interventions before the academic term begins. The solution was integrated into the institutional ASES system through an API, allowing real-time prediction queries and the generation of personalized alerts. As such, it serves as a valuable tool for strengthening academic support strategies and enhancing student retention in high-risk areas such as mathematics.

Keywords

Authors

  • Mavelyn Sterling Londoño

    Colombia

  • Fabio Andrés Barbosa Gómez

    Colombia

  • Oscar Fernando Bedoya Leiva

    Colombia

References

Al-Alawi, L., Al Shaqsi, J., Tarhini, A, & Al-Busaidi, Adil. (2023). Using machine learning to predict factors affecting academic performance: the case of college students on academic probation. Education and Information Technologies. 28. 12407-12432. https://doi.org/10.1007/s10639-023-11700-0.

Aulck, L., Velagapudi, N., Blumenstock, J., & West, J. (2016). Predicting student dropout in higher education. arXiv preprint arXiv:1606.06364. https://doi.org/10.48550/arXiv.1606.06364.

Bravo-Agapito, J., Romero Martínez, S. J., & Pamplona, S. (2021). Early prediction of undergraduate student’s academic performance in completely online learning: A five-year study. Computers in Human Behavior, 115, Article 106595. https://doi.org/10.1016/j.chb.2020.106595.

Carvajal, P., Mosquera, J. C., & Artamonova, I. (2009). Modelos de predicción del rendimiento académico en Matemáticas I en la Universidad Tecnológica de Pereira. Scientia et Technica, 15(43), 258–263.

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953.

Contreras, L. E., Fuentes, H. J., & Rodríguez, J. I. (2020). Predicción del rendimiento académico como indicador de éxito/fracaso de los estudiantes de ingeniería, mediante aprendizaje automático. Formación Universitaria, 13(5), 233–246. https://doi.org/10.4067/S0718-50062020000500233.

Del Carpio-Mendoza, R. (2024). Predicción del rendimiento académico utilizando modelos de aprendizaje automático: Una revisión sistemática de la literatura. 593 Digital Publisher CEIT, 9(6), 1038–1054. https://doi.org/10.33386/593dp.2024.6.2797.

Deniz, E. (2024). Evaluating the effectiveness of machine learning models in predicting student academic achievement. ADBA Computer Science, 1(1), Article 16. https://doi.org/10.69882/adba.cs.2024072.

Elouafi, A., Tammouch, I., Eddarouich, S., & Touahni, R. (2025). Uncovering key factors of student performance in math: An explainable deep learning approach using TIMSS 2019 data. Information, 16(6), 480. https://doi.org/10.3390/info16060480.

Farhood, H., Joudah, I., Beheshti, A., & Muller, S. (2024). Evaluating and enhancing artificial intelligence models for predicting student learning outcomes. Informatics, 11(3), Article 46. https://doi.org/10.3390/informatics11030046.

Flores-Mendoza, C., Ardila, R., Gallegos, M., & Reátegui-Colareta, N. (2021). General intelligence and socioeconomic status as strong predictors of student performance in Latin American schools: Evidence from PISA items. Frontiers in Education, 6, Article 632289. https://doi.org/10.3389/feduc.2021.632289.

Kotsiantis, S. B., Pierrakeas, C., & Pintelas, P. (2010). Predicting students’ performance in distance learning using machine learning techniques. Applied Artificial Intelligence, 18(5), 411–426. https://doi.org/10.1080/08839510490442058.

Kowalski, A. (2024). Evaluating the effectiveness of deep learning models in student performance prediction. International Academic Journal of Science and Engineering, 11(4), 1–4. https://doi.org/10.71086/IAJSE/V11I4/IAJSE1162.

Lou, Y., & Colvin, K. F. (2025). Performance prediction using educational data mining techniques: A comparative study. Discover Education, 4, 112. https://doi.org/10.1007/s44217-025-00502-w.

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems, 30, 4765–4774. https://doi.org/10.48550/arXiv.1705.07874.

OECD. (2019). Benchmarking higher education system performance. OECD Publishing. https://doi.org/10.1787/be5514d7-en.

Oppong, S. O. (2023). Predicting students’ performance using machine learning algorithms: A review. Asian Journal of Research in Computer Science, 16(3), 128–148. https://doi.org/10.9734/AJRCOS/2023/v16i3351.

Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications, 41(4), 1432–1462. https://doi.org/10.1016/j.eswa.2013.08.042.

Romero, C., & Ventura, S. (2024). Educational data mining and learning analytics: An updated survey. arXiv preprint. https://arxiv.org/abs/2402.07956.

Sangsawang, T., & Yang, L. (2025). Predicting student achievement using socioeconomic and school-level factors. Artificial Intelligence in Learning, 1(1), 20–34. https://doi.org/10.63913/ail.v1i1.4.

Slater, S., & Baker, R. S. J. d. (2019). Forecasting future student mastery. Distance Education, 40(1), 25–38. https://doi.org/10.1080/01587919.2019.1632169.

Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior, 47, 157–167. https://doi.org/10.1016/j.chb.2014.05.038.

UNESCO. (2020). Global education monitoring report 2020: Inclusion and education – All means all. United Nations Educational, Scientific and Cultural Organization.

Wang, S., & Luo, B. (2024). Academic achievement prediction in higher education through interpretable modeling. PLOS ONE, 19(9), e0309838. https://doi.org/10.1371/journal.pone.0309838.

World Bank. (2017). World development report 2018: Learning to realize education’s promise. The World Bank. https://doi.org/10.1596/978-1-4648-1096-1.

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