Bayesian Growth Curve Modelling of Student Academic Trajectories: The Impact of Individual-Level Characteristics and Implications for Education Policy

Abstract

This study examined the factors influencing student academic performance in the Bachelor of Science (BSc) Actuarial Science programme, focusing on Grade 12 mathematics marks, admission point (AP) scores, socioeconomic background, and progression rates. The purpose of this research was to identify the predictors of academic success and the disparities in performance related to socioeconomic factors. Data were obtained from 770 student records, with particular emphasis on admission point (AP) scores, Grade 12 mathematics marks, and progression rates over time. Bayesian hierarchical modelling was applied to analyse how these factors contribute to the students’ academic trajectories. The results revealed a strong correlation between Grade 12 mathematics marks and university success in Actuarial Science. Furthermore, students from lower SES backgrounds tended to perform less favourably than their peers, suggesting persistent disparities in academic achievement. Non-academic factors, such as personal motivation and external support systems, played a crucial role in student performance. This study concludes by recommending that universities adopt more holistic support strategies, incorporating both academic and non-academic interventions, to address the diverse needs of students in rigorous academic programmes like Actuarial Science. Educational policymakers are urged to revise admission policies and enhance support structures to foster equity and academic excellence.

Keywords

• Actuarial Science
• educational inequality
• higher education policy
• mathematics education
• student support services