Italian Journal of Health Education, Sports and Inclusive Didactics
Advancing Learning Analytics through Quantum-Enhanced Machine Learning: A Framework for Dropout Prediction and Intervention
Abstract
Student dropout remains a persistent challenge in higher education, where timely intervention under limited data is critical. Classical machine learning models underpin most early-warning systems, but their effectiveness declines when behavioural interactions are complex and labeled data are scarce. This study examines Quantum Machine Learning (QML) for dropout prediction, grounded in Tinto's integration model and Zimmerman's self-regulated learning framework. Using OULAD, classical models, QSVMs, and hybrid stacking architectures are benchmarked across early-semester windows and low-sample regimes. XGBoost achieves strong performance as data accumulate (PR-AUC: 0.496–0.592). QSVMs outperform classical baselines at moderate sample sizes (PR-AUC up to 0.683). Hybrid stacking yields the best results (PR-AUC up to 0.682), with fusion weights revealing regime-dependent complementarity. QML is a targeted complement; hybrid architectures are the practical integration pathway.
Keywords
References
Baker, R. S., & Hawn, A. (2022). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 32(4), 1052–1092. https://doi.org/10.1007/s40593-021-00285-9
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017). Quantum machine learning. Nature, 549(7671), 195–202. https://doi.org/10.1038/nature23474
Castro-Montoya, B., Vélez-Gómez, P., Segura-Cardona, A., & French, B. F. (2025). A cultural adaptation of Tinto’s student integration theory in undergraduate students of a private university in Colombia. Cogent Education, 12(1), 2479384.
Credé, M., & Kuncel, N. R. (2008). Study habits, skills, and attitudes: The third pillar supporting collegiate academic performance. Perspectives on Psychological Science, 3(6), 425–453. https://doi.org/10.1111/j.1745-6924.2008.00089.x
Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, Article 101859. https://doi.org/10.1016/j.cedpsych.2020.101859
Gujju, Y., Matsuo, A., & Raymond, R. (2024). Quantum machine learning on near-term quantum devices: Current state of supervised and unsupervised techniques for real-world applications. Physical Review Applied, 21(6), 067001.
Jha, N. I., Ghergulescu, I., & Moldovan, A. N. (2019). OULAD MOOC Dropout and Result Prediction using Ensemble, Deep Learning and Regression Techniques. In CSEDU (2) (pp. 154-164).
Li, I. W., & Carroll, D. R. (2020). Factors influencing dropout and academic performance: An Australian higher education equity perspective. Journal of Higher Education Policy and Management, 42(1), 14–30. https://doi.org/10.1080/1360080X.2019.1649993
Martin, A. J., & Marsh, H. W. (2008). Academic buoyancy: Towards an understanding of students' everyday academic resilience. Journal of School Psychology, 46(1), 53–83. https://doi.org/10.1016/j.jsp.2007.01.002
Martin, A. J., Colmar, S. H., Davey, L. A., & Marsh, H. W. (2010). Longitudinal modelling of academic buoyancy and motivation: Do the '5Cs' hold up over time? British Journal of Educational Psychology, 80(3), 473–496. https://doi.org/10.1348/000709910X486376
Mokhati, A., Benturki, O., Benturki, A., Fennouh, R., Kecira, Z., Bernardo, M., & Fonseca, I. (2022). Conversion of argan nutshells into novel porous carbons in the scope of circular economy: adsorption performance of emerging contaminants. Applied Sciences, 12(15), 7607.
Piepenburg, J. G., & Beckmann, J. (2022). The relevance of social and academic integration for students’ dropout decisions. Evidence from a factorial survey in Germany. European Journal of Higher Education, 12(3), 255-276.
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33–40. https://doi.org/10.1037/0022-0663.82.1.33
Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students' academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. https://doi.org/10.1037/a0026838
Romdhoni, R. D., Majid, N. W. A., & Permatasari, A. F. (2026). Machine learning-based early warning for student dropout: Evidence from LMS behavioral engagement patterns in online higher education. Journal of Informatics and Vocational Education, 9(2), 55–66. https://doi.org/10.20961/joive.v9i2.3508
Salmela-Aro, K., & Read, S. (2017). Study engagement and burnout profiles among Finnish higher education students. Burnout Research, 7, 21–28. https://doi.org/10.1016/j.burn.2017.11.001
Salmela-Aro, K., & Upadyaya, K. (2014). School burnout and engagement in the context of demands–resources model. British Journal of Educational Psychology, 84(1), 137–151. https://doi.org/10.1111/bjep.12018
Samoila, M. E., & Vrabie, T. (2023). First-year seminars through the lens of Vincent Tinto's theories of student departure. A systematic review. Frontiers in educatión, 8, 1205667.
Schuld, M., & Killoran, N. (2019). Quantum machine learning in feature Hilbert spaces. Physical Review Letters, 122(4), Article 040504. https://doi.org/10.1103/PhysRevLett.122.040504
Sghir, N., Adadi, A., & Lahmer, M. (2023). Recent advances in predictive learning analytics: A decade systematic review (2012–2022). Education and Information Technologies, 28(7), 8299–8333. https://doi.org/10.1007/s10639-022-11536-0
Tinto, V. (2012). Leaving college: Rethinking the causes and cures of student attrition. University of Chicago press.
von Keyserlingk, L., Schwerter, J., Kunina-Habenicht, O., & Lauermann, F. (2026). Motivation and self-regulated learning as predictors of learning resource use in learning management systems in higher education. Educational Psychology, 46(3), 437-457.
Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82–91. https://doi.org/10.1006/ceps.1999.1016
DOI: https://doi.org/10.32043/gsd.v10i4.1833
Refbacks
- There are currently no refbacks.
Copyright (c) 2026 ITALIAN JOURNAL OF HEALTH EDUCATION, SPORT AND INCLUSIVE DIDACTICS
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Italian Journal of Health Education, Sports and Inclusive Didactics
Publisher: Cares srl – REN Press (since 2026). Previously published by Gaia srl – Edizioni Universitarie Romane (2017–2025).
ISSN: 2532-3296 E-ISSN: 3103-5744