Developing an Early Warning System with Personalized Interventions to Enhance Academic Outcomes for At-Risk Students in Taiwanese Higher Education
Yuan-Hsun Chang, F P Chen, Chien-I Lee
Abstract
Conventional academic warning systems in higher education often rely on end-of-semester grades, which severely limits opportunities for timely intervention. To address this, our interdisciplinary study developed and validated a comprehensive socio-technical framework that integrates social-cognitive theory with learning analytics. The framework combines educational data mining with culturally responsive, personalized interventions tailored to a non-Western context. A two-phase mixed-methods design was employed: first, predictive models were built using Learning Management System (LMS) data from 2,856 students across 64 courses. Second, a quasi-experimental trial (n = 48) was conducted to evaluate intervention efficacy. Historical academic performance, attendance, and assignment submission patterns were the strongest predictors, achieving a Balanced Area Under the Curve (AUC) of 0.85. The intervention, specifically adapted to Confucian educational values, yielded remarkable results: 73% of at-risk students achieved passing grades, with a large effect size for academic improvement (Cohen’s d = 0.91). These findings empirically validate a complete prediction–intervention–evaluation cycle, demonstrating how algorithmic predictions can be effectively integrated with culturally informed human support networks. This study advances socio-technical systems theory in education by bridging computer science, psychology, and educational research. It offers an actionable model for designing ethical and effective early warning systems that balance technological innovation with human-centered pedagogical values.