An automated machine learning approach for early identification of at-risk maritime students

Abstract

Machine Learning (ML) presents a significant opportunity for the field of education, including Maritime Education and Training (MET). The benefits of ML have yet to be fully realized within MET. By utilizing ML-powered methods into maritime education, institutions can better prepare future seafarers while providing accurate, state-of-the-art education tailored to individual student needs. Early identification of areas for improvement can help students and teachers enhance educational outcomes within MET. This study presents the potential of ML approaches for predicting future performance as well as for identifying at-risk maritime students at the initial stages of their degree program. By enabling early identification, institutions can more efficiently plan and execute instructional strategies.