Leveraging Machine Learning for Early Intervention in Student Academic and Emotional Well-being

Authors

  • L Varsha Department of Applied Mathematics and Computational Science, Thiagarajar College of Engineering Madurai, India
  • T Aishwariyaa Department of Applied Mathematics and Computational Science, Thiagarajar College of Engineering Madurai, India
  • R Saraswathi Meena Department of Applied Mathematics and Computational Science, Thiagarajar College of Engineering Madurai, India
  • B Surya Devi Department of Applied Mathematics and Computational Science, Thiagarajar College of Engineering Madurai, India

DOI:

https://doi.org/10.16920/jeet/2026/v39i4/26114

Keywords:

Anomaly Detection Algorithms; Psychological factors; Educational Support Systems; Machine Learning; Early Detection

Abstract

This research studies the application of anomaly detection algorithms to enhance early intervention techniques in the academic and emotional well-being of students. While previous research has focused on conventional classification models, our strategy employs machine learning methods to identify unusual patterns in student performance and behavior. Using an extensive dataset that includes cognitive and psychological factors, our approach shows promise in accurately detecting anomalies. The results suggest that machine learning can effectively address emotional and academic difficulties through proactive intervention strategies, supporting positive student outcomes. This study underscores the importance of early anomaly detection in enabling prompt and targeted interventions to improve student well-being.

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Published

2026-04-30

How to Cite

Varsha, L., Aishwariyaa, T., Meena, R. S., & Devi, B. S. (2026). Leveraging Machine Learning for Early Intervention in Student Academic and Emotional Well-being. Journal of Engineering Education Transformations, 39(4), 141–150. https://doi.org/10.16920/jeet/2026/v39i4/26114

Issue

Volume 39, Issue 4, April 2026

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

References

Minaei-Bidgoli, B., Kashy, D. A., Kortemeyer, G., & Punch, W. F. (2003). Predicting student performance: An application of data mining methods with an educational web-based system. Proceedings of the 33rd Annual Frontiers in Education Conference (FIE), Vol. 1, pp. T2A-13. IEEE.

Baradwaj, B. K., & Pal, S. (2012). Mining educational data to analyze students' performance. arXiv preprint arXiv:1201.3417.

Qiu, F., Zhu, L., Zhang, G., Sheng, X., Ye, M., Xiang, Q., & Chen, P. K. (2022). E-learning performance prediction: Mining the feature space of effective learning behavior. Entropy, 24(5), 722.

Salami, H. O., & Ibrahim, R. S. (2017). Application of data mining techniques in higher learning institutions: A review.

Lauría, E. J. (2021). Framing early alert of struggling students as an anomaly detection problem: An exploration. In Proceedings of CSEDU (Vol. 1, pp. 26–35).

Ma, X., Wu, J., Xue, S., Yang, J., Zhou, C., Sheng, Q. Z., & Akoglu, L. (2021). A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12012–12038.

Guo, T., Bai, X., Tian, X., Firmin, S., & Xia, F. (2022). Educational anomaly analytics: Features, methods, and challenges. Frontiers in Big Data, 4, 811840.

Salami, H. O., Ibrahim, R. S., & Yahaya, M. O. (2016). Detecting anomalies in students' results using decision trees. International Journal of Modern Education and Computer Science, 8(7), 31.

Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 40(6), 601–618.

Kim, D., Park, J., Chung, H. C., & Jeong, S. (2024). Unsupervised outlier detection using random subspace and subsampling ensembles of Dirichlet process mixtures. Pattern Recognition, 156, 110846.

Kotsiantis, S., Pierrakeas, C., & Pintelas, P. (2004). Predicting students' performance in distance learning using machine learning techniques. Applied Artificial Intelligence, 18(5), 411–426.

Qasrawi, R., Polo, S. P. V., Al-Halawa, D. A., Hallaq, S., & Abdeen, Z. (2022). Assessment and prediction of depression and anxiety risk factors in schoolchildren: Machine learning techniques performance analysis. JMIR Formative Research, 6(8), e32736.

Ratul, I. J., Nishat, M. M., Faisal, F., Sultana, S., Ahmed, A., & Al Mamun, M. A. (2023). Analyzing perceived psychological and social stress of university students: A machine learning approach. Heliyon, 9(6).

Zehra, S., Faseeha, U., Syed, H. J., Samad, F., Ibrahim, A. O., Abulfaraj, A. W., & Nagmeldin, W. (2023). Machine learning-based anomaly detection in NFV: A comprehensive survey. Sensors, 23(11), 5340.