An Explainable Ai Model For Early Identification Of At-Risk (Low Performance) Students In Ethiopian Tvt Colleges (The Case Of Alamata)

Abstract

Ethiopian Technical Vocational Education and Training (TVT) institutions face persistent challenges regarding students’ academic performance. At Alamata TVT College, Certificate of Competency(CoC) examination failure rates exceeded 35% in 2018 and 31% in 2019. Despite the failure rates, the college has no predictive early warning system to identify at-risk trainees before these concerning figures, no early warning systems before they fail, leaving interventions reactive. This study develops and evaluates an explainable machine learning model to classify students into three performance risk categories: high, medium, and low risk, enabling targeted early intervention in Ethiopian TVT colleges.