Sep 2026· International Journal of Data Science and Analysis· Vol 22· 0 citations· 54 references
TL;DR
An explainable and fair early warning system for predicting student withdrawal from the Open University Learning Analytics Dataset and releases BurnoutGuard, an open-source Moodle plugin that provides a reproducible and transparent framework for equity-aware early warning.
A 28-day early-warning model using engagement, assessment, and demographic data from the Open University Learning Analytics Dataset is developed, offering a template for translating learning-analytics predictions into deployable, budget-justified retention interventions.
A. Patre· International Journal of Res...· 0 citations
Student dropout rates in higher education have become a significant problem for universities worldwide, impacting both student academic achievement and institutional stability. To address this problem, this research proposes the Student Dropout Risk Prediction and Early Warning System (SDRP-EWS), a machine learning fra...
Kritanat Chungnoy, Tanatorn Tanantong, Naphat Jakkraphatcharakul et al.· Asian Health, Science and Te...· 0 citations
Student dropout in higher education remains a persistent challenge, particularly in developing contexts where structural disadvantage intersects with academic readiness. This study proposes a prescriptive analytics framework that integrates supervised learning, unsupervised profiling, and explainable artificial int...
Rodne Quijije-Castro, Alisson Constantine-Macias, Francis R. Loayza et al.· Frontiers in Education· 0 citations
Customer retention is a strategic concern for enrollment-based educational technology (EdTech) firms, where acquiring a new customer costs far more than retaining an existing one. Churn research in education, however, rests on an unresolved measurement premise: models routinely treat any post-onboarding inactivity as c...
L. Dewi, Novita Anggraini, Mamok Andri Senubekti· International Journal Of Eco...· 0 citations
It is shown that simple, transparent models operating on routinely collected gradebook data deliver actionable early-warning signals within the digital competence of most practising teachers; group-level prediction additionally protects student identity by ensuring no individual is labelled at-risk, combining predictiv...
Víctor D. Díaz Suárez, Marina Praena-Delgado, María de los Ángeles Buenavista-Ruiz et al.· Applied System Innovation· 0 citations
Student dropout remains a persistent challenge in higher education institutions, affecting academic continuity, institutional performance, and long-term socioeconomic outcomes. Early identification of at-risk students enables timely intervention and improved retention strategies. This study proposes an explainable mach...
