A MULTIVARIATE STATISTICAL AND MACHINE LEARNING ANALYSIS OF FACTORS INFLUENCING UNIVERSITY STUDENT ACADEMIC PERFORMANCE
DOI:
https://doi.org/10.69980/5nc7k059Keywords:
Academic performance, Machine learning, Multivariate analysis, Support Vector Regression, Educational data miningAbstract
The present study aimed to explore the academic, socioeconomic, demographic, and behavioral determinants of the academic performance of university students through an integrated multivariate statistical and machine learning approach. Data from 493 university students and 16 variables were analysed with the continuous outcome being overall academic performance. Using seven supervised regression algorithms, R2, MAE, MSE, and RMSE were calculated to find significant relationships, which were then identified by descriptive statistics, correlation analysis, and group comparisons. Overall performance was most highly correlated with previous academic performance (r = 0.925, p < .001) and attendance (ρ = 0.664, p < .001), and moderately negatively associated with gaming (ρ = −0.412, p < .001). All of the variance in academic performance was accounted for by the multivariate regression model, and previous performance and preparation continued to be significant. For the machine-learning models, Support Vector Regression (SVR) gave the highest overall performance (R² = 0.909; RMSE = 0.184) followed by Random Forest (R2 = 0.908). Previous academic performance was also the most important predictor, as evidenced by the feature-importance analysis. From all these results, it is concluded that using hybrid of statistical inference and machine learning is an effective and interpretable method to predict student achievement and for early identification of academically at-risk students.
References
1. Adejo, O. W., & Connolly, T. (2018). Predicting student academic performance using multi-model heterogeneous ensemble approach. Journal of Applied Research in Higher Education, 10(1), 61–75.
2. Akçapınar, G., Altun, A., & Aşkar, P. (2019). Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education, 16(1), 40. https://doi.org/10.1186/s41239-019-0172-z
3. Alsariera, Y. A., Baashar, Y., Alkawsi, G., Mustafa, A., Alkahtani, A. A., & Ali, N. (2022). Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance. Computational Intelligence and Neuroscience, 2022, 1–11. https://doi.org/10.1155/2022/4151487
4. Alshabandar, R., Hussain, A., Keight, R., & Khan, W. (2020). Students performance prediction in online courses using machine learning algorithms. 2020 International Joint Conference on Neural Networks (IJCNN), 1–7. https://ieeexplore.ieee.org/abstract/document/9207196/
5. Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: Literature review and best practices. International Journal of Educational Technology in Higher Education, 17(1), 3. https://doi.org/10.1186/s41239-020-0177-7
6. Balcioğlu, Y. S., & Artar, M. (2025). Predicting academic performance of students with machine learning. Information Development, 41(3), 896–915. https://doi.org/10.1177/02666669231213023
7. Batool, S., Rashid, J., Nisar, M. W., Kim, J., Kwon, H.-Y., & Hussain, A. (2023). Educational data mining to predict students’ academic performance: A survey study. Education and Information Technologies, 28(1), 905–971. https://doi.org/10.1007/s10639-022-11152-y
8. Cagliero, L., Canale, L., Farinetti, L., Baralis, E., & Venuto, E. (2021). Predicting student academic performance by means of associative classification. Applied Sciences, 11(4), 1420.
9. Chaka, C. (2021). Educational data mining, student academic performance prediction, prediction methods, algorithms and tools: An overview of reviews. https://www.preprints.org/frontend/manuscript/2483a8ecb51b982fec76b4be2859ace7/download_pub
10. Cui, Y., Chen, F., Shiri, A., & Fan, Y. (2019). Predictive analytic models of student success in higher education: A review of methodology. Information and Learning Sciences, 120(3–4), 208–227.
11. Dabhade, P., Agarwal, R., Alameen, K. P., Fathima, A. T., Sridharan, R., & Gopakumar, G. (2021). Educational data mining for predicting students’ academic performance using machine learning algorithms. Materials Today: Proceedings, 47, 5260–5267.
12. 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
13. Ginosyan, H., Tuzlukova, V., & Ahmed, F. (2020). An investigation into the role of extracurricular activities in supporting and enhancing students’ academic performance in tertiary foundation programs in Oman. Theory and Practice in Language Studies, 10(12), 1528–1534.
14. Gray, C. C., & Perkins, D. (2019). Utilizing early engagement and machine learning to predict student outcomes. Computers & Education, 131, 22–32.
15. Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A., Sarker, K. U., & Sattar, M. U. (2020). Predicting student performance in higher educational institutions using video learning analytics and data mining techniques. Applied Sciences, 10(11), 3894.
16. Hasan, T., Hasan, M. M., & Manzoor, T. (2024). Student Performance Metrics Dataset. 1. https://doi.org/10.17632/5b82ytz489.1
17. Jang, Y., Choi, S., Jung, H., & Kim, H. (2022). Practical early prediction of students’ performance using machine learning and eXplainable AI. Education and Information Technologies, 27(9), 12855–12889. https://doi.org/10.1007/s10639-022-11120-6
18. Kukkar, A., Mohana, R., Sharma, A., & Nayyar, A. (2023). Prediction of student academic performance based on their emotional wellbeing and interaction on various e-learning platforms. Education and Information Technologies, 28(8), 9655–9684. https://doi.org/10.1007/s10639-022-11573-9
19. Ljubičić, T., & Hell, M. (2023). Predikcija uspješnosti studenata primjenom umjetnih neuronskih mreža. Ekonomska Misao i Praksa, 32(2), 361–374.
20. Musso, M. F., Hernández, C. F. R., & Cascallar, E. C. (2020). Predicting key educational outcomes in academic trajectories: A machine-learning approach. Higher Education, 80(5), 875–894. https://doi.org/10.1007/s10734-020-00520-7
21. Rastrollo-Guerrero, J. L., Gómez-Pulido, J. A., & Durán-Domínguez, A. (2020). Analyzing and predicting students’ performance by means of machine learning: A review. Applied Sciences, 10(3), 1042.
22. Rodríguez-Hernández, C. F., Cascallar, E., & Kyndt, E. (2020). Socio-economic status and academic performance in higher education: A systematic review. Educational Research Review, 29, 100305.
23. Rodríguez-Hernández, C. F., Musso, M., Kyndt, E., & Cascallar, E. (2021). Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation. Computers and Education: Artificial Intelligence, 2, 100018.
24. Santos, R. M., & Henriques, R. (2023). Accurate, timely, and portable: Course-agnostic early prediction of student performance from LMS logs. Computers and Education: Artificial Intelligence, 5, 100175.
25. Selvitopu, A., & Kaya, M. (2023). A Meta-Analytic Review of the Effect of Socioeconomic Status on Academic Performance. Journal of Education, 203(4), 768–780. https://doi.org/10.1177/00220574211031978
26. Tomasevic, N., Gvozdenovic, N., & Vranes, S. (2020). An overview and comparison of supervised data mining techniques for student exam performance prediction. Computers & Education, 143, 103676.
27. Trakunphutthirak, R., & Lee, V. C. S. (2022). Application of Educational Data Mining Approach for Student Academic Performance Prediction Using Progressive Temporal Data. Journal of Educational Computing Research, 60(3), 742–776. https://doi.org/10.1177/07356331211048777
28. Tsiakmaki, M., Kostopoulos, G., Kotsiantis, S., & Ragos, O. (2020). Transfer learning from deep neural networks for predicting student performance. Applied Sciences, 10(6), 2145.
29. Wang, J., & Yu, Y. (2025). Machine learning approach to student performance prediction of online learning. PloS One, 20(1), e0299018.
