A Proposed Information System Model for Decision Support in Early Identification and Intervention of Technical Student Dropout Risk
DOI:
https://doi.org/10.16920/jeet/2026/v40is1/26153Keywords:
Early Warning System, Human-in-the-loop, Information System, Student Dropout.Abstract
Student dropout remains a significant challenge in engineering and technical higher education, where technical students are expected to meet demanding academic, practical, technological, and professional learning requirements. This study proposes an educational information system model for the early identification of technical students at risk of dropout and for supporting timely academic intervention. The study combines a Systematic Literature Review (SLR) guided by PRISMA with Design Science Research Methodology (DSRM) to develop the proposed model as a research artifact. The literature synthesis shows that existing dropout models remain limited in integrating academic and non-academic data, developing interpretable multidimensional risk profiles, and translating predictive outputs into actionable intervention recommendations. The proposed model integrates academic records, Learning Management System activity, socioeconomic conditions, psychological indicators, social support, and institutional information with risk profiling, hybrid machine learning, Explainable Artificial Intelligence, prescriptive analytics, and human-in-the-loop validation. Rather than treating dropout prediction as a purely computational task, the model positions educational data and artificial intelligence as decision-support tools for identifying risk factors, informing academic interventions, and maintaining human oversight in final decisions. The study contributes a human-centred decision-support framework for proactive academic advising and student progression in engineering and technical education.
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