A Comprehensive Review of Machine Learning Techniques for Student Academic Performance Prediction

Abstract

Student academic performance prediction is a crucial topic in Educational Data Mining (EDM) and Learning Analytics, which can be used to help at-risk students by undertaking timely actions. The given paper is a systematic review of machine learning methods used in this field. It analyzes a range of approaches, starting with interpretable models such as Multiple Linear Regression and Decision Trees to ensemble and deep learning high-performance models such as Random Forest and Neural Networks. The review highlights the central role of feature engineering and is discussing predictors of academic and behavioral data, social-economic and psychological conditions. One of the broad implications of this paper is providing a comparative analysis of these methods with an emphasis on the continuing trade-off between predictive accuracy and model inter-pretability. Moreover, the disconnect between theory and real-world, full-stack deployment systems, which are more and more critical when it comes to actual usability, is also critically discussed in this review. Major gaps in the research, such as excessive use of synthetic data, lack of practical testing, and ethics, are determined. Lastly, the paper presents future directions which include the use of Explainable AI (XAI), federated learning in privacy and creation of real-time adaptive feedback systems.

Introduction

Traditional education systems often rely on reactive assessment, identifying struggling students too late. In contrast, Educational Data Mining (EDM) and Learning Analytics (LA) use ML to analyze student data and enable early prediction of at-risk students, allowing timely interventions that improve retention and performance.

The literature highlights various ML approaches. Traditional methods like Linear Regression and Decision Trees are valued for their interpretability but may lack accuracy or stability. More advanced models such as Random Forest and Support Vector Machines offer higher accuracy but are less interpretable. Deep learning models, including Artificial Neural Networks and RNNs/LSTMs, can capture complex patterns and time-based trends but suffer from low transparency, high data requirements, and implementation complexity.

A key challenge is selecting appropriate features, as student performance depends on academic, behavioral, socio-economic, and psychological factors. However, many models focus mainly on academic data, limiting predictive quality and fairness.

Another major issue is the gap between theoretical ML models and practical deployment. Recent work emphasizes full-stack systems using frameworks like Flask or FastAPI with dashboards to make predictions usable in real educational settings.

A central theme in the discussion is the trade-off between accuracy and interpretability. Highly accurate models are often “black boxes,” while simpler models are more transparent but less powerful. Despite advances, linear models remain widely used due to their simplicity, speed, and explainability. Deep learning adoption remains limited due to technical, data, and ethical challenges.

Overall, the study concludes that while ML has strong potential to transform education through early prediction systems, challenges in interpretability, feature selection, and real-world implementation must be addressed for broader adoption.

Conclusion

This paper has provided a comprehensive review of the machine learning techniques used for student academic perfor- mance prediction. The analysis reveals a dynamic field charac- terized by a fundamental tension between model accuracy and interpretability. While complex ensemble and deep learning models offer superior predictive power, the transparency and practicality of traditional methods like Multiple Linear Regres- sion ensure their continued relevance, especially in deployed systems. A significant gap persists between models developed in a research context and those validated in real-world educational settings. Future work must prioritize real-world validation, address the ethical dimensions of algorithmic prediction, and integrate Explainable AI to build systems that are not only accurate but also trustworthy and actionable for educators. The ultimate goal is to augment human judgment, providing data- driven insights that enable more effective and equitable support for all students.

References

[1] C. Romero and S. Ventura, “Educational data mining: A survey from 1995 to 2005,” Expert Systems with Applications, vol. 33, no. 1, pp. 135–146, 2007. [2] Kotsiantis, “Educational data mining: A review of the state of the art,” University of Peloponnese, 2007. [3] T. M. Kaur and S. Sharma, “Student performance prediction using ma- chine learning techniques,” Int. J. of Recent Technology and Engineering, vol. 8, no. 4, pp. 2277–2281, 2019. [4] F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011. [5] G. Siemens and R. S. Baker, “Learning analytics and educational data mining: Towards communication and collaboration,” in Proc. 2nd Int. Conf. on Learning Analytics and Knowledge, 2012, pp. 252–254. [6] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 4765–4774. [7] FastAPI Framework Documentation, 2024. [Online]. Available: https://fastapi.tiangolo.com/ [8] N. (nikhil7280), “Student Performance (Multiple Linear Regression),” Kaggle, 2023. [Online]. Available: https://www.kaggle.com/datasets/ nikhil7280/student-performance-multiple-linear-regression [9] M. Hussain, W. Zhu, W. Zhang, and S. M. R. Abidi, “Student engage- ment predictions in an e-learning system and their impact on student performance,” Computational Intelligence and Neuroscience, vol. 2018, 2018. [10] 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. [11] O. Oyerinde and C. Chia, “Predicting students’ academic performances— A learning analytics approach using multiple linear regression,” Int.J. of Computer Applications, vol. 157, no. 4, pp. 37–44, 2017. [12] A. Rastrollo-Guerrero, J. A. Go´mez-Pulido, and A. Dura´n-Dom´?nguez, “Analyzing and predicting students’ performance by means of machine learning: A review,” Applied Sciences, vol. 10, no. 3, p. 1042, 2020. [13] D. Kabakchieva, “Predicting student performance by using data mining methods for classification,” Cybernetics and Information Technologies, vol. 13, no. 1, pp. 61–72, 2013. [14] S. Aulck, N. Velagapudi, J. Blumenstock, and J. West, “Predicting student dropout in higher education,” arXiv preprint arXiv:1606.06364, 2016 [15] C. Romero, S. Ventura, and E. Garc´?a, “Data mining in course manage- ment systems: Moodle case study and tutorial,” Computers & Education, vol. 51, no. 1, pp. 368–384, 2008. [16] V. Belle and I. Papantonis, “Principles and practice of explainable artificial intelligence,” Frontiers in Big Data, vol. 4, p. 688969, 2021 [17] M. T. H. Alyahyan and D. Du¨s¸tego¨r, “Predicting academic success in higher education: literature review and best practices,” International Journal of Educational Technology in Higher Education, vol. 17, no. 1, p. 3, 2020. [18] I. E. Livieris, K. Drakopoulou, and P. Pintelas, “A CNN-LSTM model for dropout prediction in e-learning,” in 2020 11th International Con- ference on Information, Intelligence, Systems and Applications (IISA), 2020, pp. 1-6. [19] A. J. Al-Radaideh, E. M. Al-Shawakfa, and M. I. Al-Najjar, “Mining student data using decision trees,” in 2006 International Arab Confer- ence on Information Technology, 2006. [20] B. M. F. Al-Shargabi, F. H. Al-Hadhrami, and A. A. Al-Dhaqm, “A systematic review of machine learning techniques for students’ academic performance prediction,” IEEE Access, vol. 11, pp. 69022-69040, 2023. [21] R. S. Baker and K. H. Yacef, “The state of educational data mining in 2009: A review and future visions,” Journal of Educational Data Mining, vol. 1, no. 1, pp. 3-17, 2009. [22] S. Li, Y. Liu, and Q. Yang, “Federated learning for privacy-preserving collaborative learning in education,” in Proceedings of the 2021 AAAI Spring Symposium on AI for Education, 2021.

Copyright

Copyright © 2026 Dr. P. C. Khanzode, Shubham Dande, Nishant Sonone, Abhinav Surjuse, Amit Khadse, Shreyash Kuyate. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.