A WEB-BASED MACHINE LEARNING MODEL FOR PREDICTING STUDENT ACADEMIC PERFORMANCE IN TERTIARY INSTITUTIONS

Authors

  • EMMANUEL JOHN ANAGU Information System Department, Federal University of Lagos, Nigeria.
  • Rande Wella Department of Information Technology, Taraba state Polytenich Suntai, Nigeria

DOI:

https://doi.org/10.54554/jacta.2025.07.01.005

Abstract

Educational data mining plays a crucial role in analyzing student performance to identify those at risk and enhance academic success. Traditional statistical methods often fail to capture the complex factors influencing student achievement. This research presents a machine learning-based predictive system integrated into a web application to forecast student academic performance. The study utilizes a dataset from Taraba State University, comprising students from diverse demographics. The dataset undergoes preprocessing, feature selection, and modeling using three machine learning algorithms: Random Forest, Support Vector Machine (SVM), and Decision Tree. The evaluation results demonstrate that Random Forest achieved the highest accuracy (94%), followed by SVM (93%) and Decision Tree (92%). The developed web-based system allows educators to input student data and receive real-time performance predictions, facilitating early intervention strategies. The study highlights the potential of machine learning in educational decision-making and recommends further research on ensemble learning techniques for real-time academic performance prediction.

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References

A. A. Saa, M. Al-Emran, and K. Shaalan, "Mining student information system records to

predict students’ academic performance," in Proc. Int. Conf. Adv. Mach. Learn., 2020.

M. V. Martins, L. Baptista, J. Machado, and V. Realinho, "Multi-class phased prediction of

academic performance and dropout in higher education," Appl. Sci., vol. 13, p. 4702, 2023.

H. A. Mengash, "Using data mining techniques to predict student performance to support

decision making in university admission systems," IEEE Access, vol. 8, pp. 55462-55470,

A. M. Adeyemi and S. B. Adeyemi, "Institutional factors as predictors of students’ academic

achievement in colleges of education in South Western Nigeria," Int. J., 2014.

V. E. Adeyemo, A. Abdullah, N. Z. JhanJhi, M. Supramaniam, and A. O. Balogun,

"Ensemble and deep-learning methods for two-class and multi-attack anomaly intrusion

detection: An empirical study," Int. J. Adv. Comput. Sci. Appl., vol. 10, 2019.

S. A. Oyebade and C. Dike, "Restructuring Nigerian tertiary (university) education for better

performance," presented at the 11th Annu. Meeting Bulg. Comp. Educ. Soc., Plovdiv,

Bulgaria, 2013.

K. Taherkhani, C. Eischer, and E. Toyserkani, "An unsupervised machine learning

algorithm for in-situ defect-detection in laser powder-bed fusion," J. Manuf. Process., vol.

, pp. 476-489, 2022.

D. Krotov and J. J. Hopfield, "Unsupervised learning by competing hidden units," Proc.

Natl. Acad. Sci., vol. 116, pp. 7723–7731, 2019.

D. Silver et al., "A general reinforcement learning algorithm that masters chess, shogi, and

Go through self-play," Science, vol. 362, pp. 1140–1144, 2018.

O. J. Abiodun and A. I. Wreford, "Students’ performance evaluation using ensemble

machine learning algorithms," Eng. Technol. J., vol. 9, 2024, doi: 10.47191/etj/v9i08.23.

K. Alalawi, R. Athauda, and R. Chiong, "Contextualizing the current state of research on the use of machine learning for student performance prediction: A systematic literature review,"

Eng. Rep., vol. 5, p. e12699, 2023.

W. Zhang, Y. Wang, and S. Wang, "Predicting academic performance using tree-based

machine learning models: A case study of bachelor students in an engineering department

in China," Educ. Inf. Technol., vol. 27, pp. 13051-13066, 2022.

B. K. Francis and S. S. Babu, "Predicting academic performance of students using a hybrid

data mining approach," J. Med. Syst., vol. 43, p. 162, 2019.

H. Waheed et al., "Predicting academic performance of students from VLE big data using

deep learning models," Comput. Hum. Behav., vol. 104, p. 106189, 2020.

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Published

2025-07-30

How to Cite

ANAGU, E. J., & Wella, R. (2025). A WEB-BASED MACHINE LEARNING MODEL FOR PREDICTING STUDENT ACADEMIC PERFORMANCE IN TERTIARY INSTITUTIONS. Journal of Advanced Computing Technology and Application (JACTA), 7(1), 55–67. https://doi.org/10.54554/jacta.2025.07.01.005

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