Predictive Modeling of Academic Success in Financial Mathematics Using Machine Learning Techniques

This study develops and evaluates predictive models for student performance in Financial Mathematics (ASC303) using Machine Learning techniques. Building on prior research that established key relationships between prerequisite courses and ASC303 performance, the research analyses academic records of 226 Diploma in Actuarial Science students. The research applies Association Rule Mining (ARM) to identify critical course dependencies by evaluating both pre-university (SPM) qualifications and prerequisite courses. The ARM results reveal 3,385 significant patterns with strong predictive accuracy, showing that SPM Additional Mathematics and Mathematics grades significantly influence ASC303 performance. High-performing SPM students demonstrate 3.2 times greater likelihood of excelling in Financial Mathematics. The findings also highlight the critical role of early mathematical proficiency, particularly in Calculus and Probability during semesters preceding ASC303 in determining successful performance. The results further highlight the lasting impact of pre-university STEM preparation, indicating the need for targeted interventions to address knowledge gaps. The predictive models also enable early identification of at-risk students, providing a 2-3 semester gap for targeted support. Methodologically, the study establishes a novel framework that combines ARM’s interpretability with machine learning’s predictive power, addressing a significant gap in STEM education analytics. Future studies should expand datasets to improve generalization and investigate additional predictive factors.