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Original Article
An Explainable Machine Learning Framework for Early Prediction of Student Academic Performance
Deepak Kumar1
Vikas Arora2
Abhinav Pratap Soni3
Anurag Agarwal4
1 2 3 4Department of Computer Science Engineering, Roorkee Institute of Technology, Uttarakhand, India.
Published Online: March-April 2026
Pages: 90-95
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260702012
References
1. R. Katarya and R. Meena, “A systematic review on predicting the performance of students in higher education using machine learning
techniques,” Wireless Personal Communications, Springer, vol. 136, pp. 1–29, 2024, doi: 10.1007/s11277-023-10838-x.
2. B. Albreiki, N. Zaki, and H. Alashwal, “A systematic literature review of student performance prediction using machine learni ng
techniques,” Education Sciences, vol. 11, no. 9, p. 552, 2021, doi: 10.3390/educsci11090552.
3. A. Namoun and A. Alshanqiti, “Predicting student performance using data mining and learning analytics techniques: A systematic
literature review,” Applied Sciences, vol. 11, no. 1, p. 237, 2021, doi: 10.3390/app11010237.
4. M. Yağcı, “Educational data mining: Prediction of students’ academic performance using machine learning algorithms,” Smart
Learning Environments, vol. 9, no. 1, pp. 1–18, 2022, doi: 10.1186/s40561-022-00192-z.
5. S. Kumar, A. Sharma, and R. Verma, “Student performance prediction using data mining classification algorithms,” Advances in
Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 17–25, 2024.
6. Y. Wang, A. Ding, K. Guan, S. Wu, and Y. Du, “Graph-based ensemble machine learning for student performance prediction,” arXiv
preprint arXiv:2112.07893, 2021.
7. A. G. R. Sandeepa and S. Mohottala, “Evaluation of machine learning models in student academic performance prediction,” arXiv
preprint arXiv:2506.08047, 2025.
8. J. R. Quinlan, “Induction of decision trees,” Machine Learning, vol. 1, no. 1, pp. 81–106, 1986.
9. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
10. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, Cambridge, MA, USA, 2016.
11. S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” Advances in Neural Information Processing
Systems (NeurIPS), vol. 30, pp. 4765–4774, 2017.
12. S. M. Lundberg et al., “From local explanations to global understanding with explainable AI for trees,” Nature Machine Intelligence,
vol. 2, pp. 56–67, 2020, doi: 10.1038/s42256-019-0138-9.
13. P. Cortez and A. Silva, “Using data mining to predict secondary school student performance,” in Proceedings of the 5th Annual Future
Business Technology Conference (FUBUTEC), Porto, Portugal, 2008, pp. 5–12.
14. P. Cortez, “Student Performance Data Set,” UCI Machine Learning Repository, University of California, Irvine, 2008. [Online].
Available: https://archive.ics.uci.edu/ml/datasets/student+performance
techniques,” Wireless Personal Communications, Springer, vol. 136, pp. 1–29, 2024, doi: 10.1007/s11277-023-10838-x.
2. B. Albreiki, N. Zaki, and H. Alashwal, “A systematic literature review of student performance prediction using machine learni ng
techniques,” Education Sciences, vol. 11, no. 9, p. 552, 2021, doi: 10.3390/educsci11090552.
3. A. Namoun and A. Alshanqiti, “Predicting student performance using data mining and learning analytics techniques: A systematic
literature review,” Applied Sciences, vol. 11, no. 1, p. 237, 2021, doi: 10.3390/app11010237.
4. M. Yağcı, “Educational data mining: Prediction of students’ academic performance using machine learning algorithms,” Smart
Learning Environments, vol. 9, no. 1, pp. 1–18, 2022, doi: 10.1186/s40561-022-00192-z.
5. S. Kumar, A. Sharma, and R. Verma, “Student performance prediction using data mining classification algorithms,” Advances in
Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 17–25, 2024.
6. Y. Wang, A. Ding, K. Guan, S. Wu, and Y. Du, “Graph-based ensemble machine learning for student performance prediction,” arXiv
preprint arXiv:2112.07893, 2021.
7. A. G. R. Sandeepa and S. Mohottala, “Evaluation of machine learning models in student academic performance prediction,” arXiv
preprint arXiv:2506.08047, 2025.
8. J. R. Quinlan, “Induction of decision trees,” Machine Learning, vol. 1, no. 1, pp. 81–106, 1986.
9. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
10. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, Cambridge, MA, USA, 2016.
11. S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” Advances in Neural Information Processing
Systems (NeurIPS), vol. 30, pp. 4765–4774, 2017.
12. S. M. Lundberg et al., “From local explanations to global understanding with explainable AI for trees,” Nature Machine Intelligence,
vol. 2, pp. 56–67, 2020, doi: 10.1038/s42256-019-0138-9.
13. P. Cortez and A. Silva, “Using data mining to predict secondary school student performance,” in Proceedings of the 5th Annual Future
Business Technology Conference (FUBUTEC), Porto, Portugal, 2008, pp. 5–12.
14. P. Cortez, “Student Performance Data Set,” UCI Machine Learning Repository, University of California, Irvine, 2008. [Online].
Available: https://archive.ics.uci.edu/ml/datasets/student+performance
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