Interpretable Predictive Modeling for Educational Equity: A Workload-Aware Decision Support System for Early Identification of At-Risk Students
Shaikhanova A. Kuznetsov O. Iklassova K. Tokkuliyeva A. Sugurova L.
November 2025Multidisciplinary Digital Publishing Institute (MDPI)
Big Data and Cognitive Computing
2025#9Issue 11
Educational equity and access to quality learning opportunities represent fundamental pillars of sustainable societal development, directly aligned with the United Nations Sustainable Development Goal 4 (Quality Education). Student retention remains a critical challenge in higher education, with early disengagement strongly predicting eventual failure and limiting opportunities for social mobility. While machine learning models have demonstrated impressive predictive accuracy for identifying at-risk students, most systems prioritize performance metrics over practical deployment constraints, creating a gap between research demonstrations and real-world impact for social good. We present an accountable and interpretable decision support system that balances three competing objectives essential for responsible AI deployment: ultra-early prediction timing (day 14 of semester), manageable instructor workload (flagging 15% of students), and model transparency (multiple explanation mechanisms). Using the Open University Learning Analytics Dataset (OULAD) containing 22,437 students across seven modules, we develop predictive models from activity patterns, assessment performance, and demographics observable within two weeks. We compare threshold-based rules, logistic regression (interpretable linear modeling), and gradient boosting (ensemble modeling) using temporal validation where early course presentations train models tested on later cohorts. Results show gradient boosting achieves AUC (Area Under the ROC Curve, measuring discrimination ability) of 0.789 and average precision of 0.722, with logistic regression performing nearly identically (AUC 0.783, AP 0.713), revealing that linear modeling captures most predictive signal and makes interpretability essentially free. At our recommended threshold of 0.607, the predictive model flags 15% of students with 84% precision and 35% recall, creating actionable alert lists instructors can manage within normal teaching duties while maintaining accountability for false positives. Calibration analysis confirms that predicted probabilities match observed failure rates, ensuring trustworthy risk estimates. Feature importance modeling reveals that assessment completion and activity patterns dominate demographic factors, providing transparent evidence that behavioral engagement matters more than student background. We implement a complete decision support system generating instructor reports, explainable natural language justifications for each alert, and personalized intervention templates. Our contribution advances responsible AI for social good by demonstrating that interpretable predictive modeling can support equitable educational outcomes when designed with explicit attention to timing, workload, and transparency—core principles of accountable artificial intelligence.
accountability in AI , decision support system , early warning system , explainable artificial intelligence , interpretable machine learning , learning analytics , predictive modeling , quality education , student success prediction
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Department of Information Security, L.N. Gumilyov Eurasian National University, Satpayev 2, Astana, 010008, Kazakhstan
Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, CO, Novedrate, 22060, Italy
Department of Intelligent Software Systems and Technologies, School of Computer Science and Artificial Intelligence, Karazin Kharkiv National University, 4 Svobody Sq., V. N, Kharkiv, 61022, Ukraine
Department of Information and Communication Technologies, Manash Kozybayev North Kazakhstan University, Pushkin Str., 86, Petropavlovsk, 150000, Kazakhstan
Department of Automation and Telecommunications, M.Kh. Dulati Taraz University, Tole bi 60, Taraz, 080002, Kazakhstan
Department of Information Security
Department of Theoretical and Applied Sciences
Department of Intelligent Software Systems and Technologies
Department of Information and Communication Technologies
Department of Automation and Telecommunications
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