Machine Learning-Based Prediction of Students' Academic Performance in Higher Education

In higher education, student academic performance is considered a key educational outcome. Consequently, improving academic performance is a pressing objective for many higher education institutions. Predicting and classifying student performance can provide valuable insights and facilitate the development of appropriate strategies to enhance academic results. In recent years, data mining methods have been widely applied and have demonstrated significant success in classification and prediction tasks. This article presents and analyzes the EASP student performance dataset. Seven machine learning methods were applied to classify and predict student academic performance: Multinomial Naïve Bayes, Support Vector Machines, Decision Trees, Logistic Regression, Random Forest, Gradient Boosting, and k-Nearest Neighbors. The performance of these methods was evaluated using precision, recall, the area under the ROC curve (AUC), and the F1-score. Experimental results demonstrated that Logistic Regression achieved the best predictive performance on the EASP test set, with a precision of 0.75.

1. Rasli A., Fei Z., Abas I. H., Tee M., Prestianawati S. A., Lajuma S., et al. (2024) A Framework for Higher Education Institutions Sustainability: A Multi-Method Study. Journal of Infrastructure, Policy and Development. 8 (5). https://doi.org/10.24294/jipd.v8i5.3566.

2. Daka H., Changwe R. (2020) An Exploration of Education Quality in the Light of the Grade Point Average and Examination Attrition Rate. International Journal of Humanities Social Sciences and Education. 7 (6), 196–207. http://dx.doi.org/10.20431/2349-0381.0706021.

3. Zou T., Zhang Y., Zhou B. (2022) Does GPA Matter for University Graduates' Wages? New Evidence Revisited. PLoS One. 17 (4). https://doi.org/10.1371/journal.pone.0266981.

4. Bai C. E., Chi W., Qian X. (2014) Do College Entrance Examination Scores Predict Undergraduate GPAs? A Tale of Two Universities. China Economic Review. 30, 632–647.

5. Meda S. A., Gueorguieva R. V., Pittman B., Rosen R. R., Aslanzadeh F., Tennen H., et al. (2017) Longitudinal Influence of Alcohol and Marijuana Use on Academic Performance in College Students. PLoS One. 12 (3). https://doi.org/10.1371/journal.pone.0172213.

6. Wider W., Tanucan J. C. M., Lin J., Jiang L., Udang L. N. (2023) Who Makes a Better University Adjustment Wingman: Parents or Friends? PLoS One. 18 (12). https://doi.org/10.1371/journal.pone.0294658.

7. Bottia M. C., Giersch J., Mickelson R. A., Stearns E., Moller S. (2015) Distributive Justice Antecedents of Race and Gender Disparities in First-Year College Performance. Social Justice Research. 29 (1), 35–72.

8. Nonis S. A., Hudson G. I. (2006) Academic Performance of College Students: Influence of Time Spent Studying and Working. Journal of Education for Business. 81 (3), 151–159.

9. Albreiki B., Zaki N., Alashwal H. (2021) A Systematic Literature Review of Students' Performance Prediction Using Machine Learning Techniques. Education Sciences. 11 (9). https://doi.org/10.3390/educsci11090552.

10. Yağcı M. (2022) Educational Data Mining: Prediction of Students' Academic Performance Using Machine Learning Algorithms. Smart Learning Environments. 9. https://doi.org/10.1186/s40561-022-00192-z.

11. Dutt A., Ismail M. A., Herawan T. (2017) A Systematic Review on Educational Data Mining. IEEE Access. 5, 15991–16005. https://doi.org/10.1109/ACCESS.2017.2654247.

12. Mansur B. F., Yusof N. (2018) The Latent of Student Learning Analytic with K-Mean Clustering for Student Behaviour Classification. Journal of Information Systems Engineering and Business Intelligence. 4 (2), 156–161. https://doi.org/10.20473/JISEBI.4.2.156-161.

13. Hasan H. M. R., Rabby A. S. A., Islam M. T., Hossain S. A. (2019) Machine Learning Algorithm for Student's Performance Prediction. 2019 10th International Conference on Computing, Communication and Networking Technologies. 1–7. https://doi.org/10.1109/ICCCNT45670.2019.8944629.

14. Salah H. A., Akeel A. W., Khalaf H. A. (2020) Student Performance Prediction Model Based on Supervised Machine Learning Algorithms. IOP Conference Series: Materials Science and Engineering. 928 (3). https://doi.org/10.1088/1757-899X/928/3/032019.

15. Effects of Alcohol on Student Performance Dataset. Available: https://www.kaggle.com/datasets/joshuanaude/effects-of-alcohol-on-student-performance.