## 1 Introduction
### Reviewed Literature  
This research reviewed studies that explored factors influencing student academic performance, focusing on themes of admission point scores, socioeconomic background, progression rates, and variability across academic programmes. A particular emphasis is placed on the Bachelor of Science (BSc) Actuarial Science programme to identify gaps in the existing research and set the context for the present study. Admission point (AP) scores and prior academic achievements, such as Grade 12 mathematics marks, are commonly used predictors of university success. Studies have shown a strong association between these criteria and students' academic trajectories. For instance, ref. [19] found that general high school knowledge predicted success more reliably than programme-specific entrance exams at the Budapest University of Technology and Economics. Their findings indicate that high school performance provides a broad measure of academic ability, which is beneficial for success in university programmes that require rigorous quantitative skills, such as Actuarial Science. Similarly, ref. [20] explored predictive validity in medical education, demonstrating that high school grades and standardised test scores significantly correlate with university cumulative grade point average (GPA). The implications for Actuarial Science are significant given the quantitative nature of the field, suggesting that a solid performance in high school mathematics could serve as a strong predictor of university achievement. However, there are limitations to using AP scores and standardised tests as the sole basis for admission. Ref. [21] noted that students with lower placement exam scores but high school averages still performed well in coursework, highlighting the limitations of standardised assessments in capturing the complete picture of a student's potential. This suggests a need for a more nuanced approach that incorporates multiple indicators, such as learning strategies, socioeconomic background, and foundational preparation. Despite evidence supporting the predictive power of AP scores, there is a lack of research examining how these scores interact with other factors, such as course-specific demands or the impact of varying instructional quality across modules. This study uses Bayesian hierarchical modelling to fill this gap by exploring the influence of AP scores in conjunction with other characteristics, such as socioeconomic background and module-level variability. Socioeconomic status (SES) and related indicators, like school quintile and parental education, significantly influence academic outcomes. Ref. [22] demonstrated the impact of sociodemographic factors on student performance across various countries using data from the Programme for International Student Assessment (PISA). The study found significant differences between the schools in outcomes such as achievement and learning strategies, with sociodemographic factors accounting for a considerable portion of the variation. This variability poses challenges when generalising academic expectations across diverse populations. Furthermore, ref. [23] identified persistent achievement gaps among students from different racial, socioeconomic, and geographic backgrounds, particularly in mathematics. Students from high-poverty schools and underrepresented racial groups tended to show lower academic growth during the school year and higher learning loss over the summer compared to their more advantaged peers. These disparities underscore the need for tailored interventions to bridge the gap in academic readiness, especially for demanding fields like Actuarial Science, where a solid mathematical foundation is critical. While the literature has extensively documented the effects of SES on academic achievement, limited attention has been given to how these factors influence specific academic trajectories within quantitative fields like Actuarial Science.