ORIGINAL RESEARCH article
Front. Educ.
Sec. Higher Education
This article is part of the Research TopicPredicting Student Retention and Success in Higher EducationView all 4 articles
Ex-ADA: A SHAP-based Explainable AdaBoost Framework for Predicting At-Risk Students
Provisionally accepted
Emrah Arslan1
Silvia Gaftandzhieva2*Ali Gorgani Firouzjaei3
Javad Hassannataj Joloudari4Rositsa Doneva2- 1KTO Karatay Universitesi, Konya, Türkiye
- 2Plovdivski universitet Paisij Hilendarski, Plovdiv, Bulgaria
- 3Tarbiat Modares University Faculty of Management and Economics, Tehran, Iran
- 4University of Birjand, Birjand, Iran
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Early identification of academically at-risk students remains a persistent challenge in higher education, largely due to the limited explainability and adaptability of existing predictive models. Although many early-warning systems rely on behavioral, assessment, or attendance data, their lack of transparent decision-making often reduces trust and limits their practical utility for educators. To address this problem, this study proposes Ex-ADA, an Explainable AdaBoost-based framework that integrates the interpretive strength of SHapley Additive exPlanations (SHAP) with the robust ensemble learning capabilities of AdaBoost. Using academic, behavioral, and engagement indicators from 642 students enrolled in the Fundamentals of Programming course at the University of Plovdiv, the framework aims to deliver both high predictive accuracy and human-interpretable insights for data-driven intervention. Ex-ADA achieves an accuracy of 84.12% and an AUC of 92.31%, outperforming conventional classifiers such as k-nearest neighbor, decision tree, naïve Bayes, and multilayer perceptron. SHAP This is a provisional file, not the final typeset article analyses reveal that attendance, midterm practice performance, and homework completion are the most influential predictors of student success. In addition to global interpretability, the framework provides personalized, instance-level explanations that help instructors understand each student's risk factors. By bridging predictive analytics with transparent educational decision-making, Ex-ADA demonstrates how explainable ensemble models can enhance early-warning systems and support more effective, timely pedagogical interventions.
Keywords: At-risk student prediction, Student performance prediction, Educational datamining, Explainable artificial intelligence, Adaboost, Shapley additive explanations
Received: 19 Oct 2025; Accepted: 08 Dec 2025.
Copyright: © 2025 Arslan, Gaftandzhieva, Gorgani Firouzjaei, Hassannataj Joloudari and Doneva. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Silvia Gaftandzhieva
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