Bayesian Hierarchical Modelling of Student Academic Performance

The Impact of Mathematics Competency, Institutional Context, and Temporal Variability

Bibliographic Data

ID22048902
AuthorsMoeketsi Mosia (0000-0002-7189-0018, University of the Free State), Egara Felix Oromena (0000-0002-6249-9615, University of the Free State, corresponding author), Fadip Audu Nannim (0000-0002-6452-5602, University of the Free State), Moses Basitere (0000-0002-1258-5443, University of Cape Town)
Year2025
Volume15
Issue2
Pages177
Publication date2025-02-03
Open AccessYes
TypeARTICLE
VenueEducation Sciences (JOURNAL)
Journal identifiersISSN: 2227-7102 • E-ISSN: 2227-7102
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci15020177
OpenAlexW4407106629
LanguageEN
References cited38

This study explores the multifaceted factors influencing academic performance among undergraduate students enrolled in Science, Technology, Engineering, and Mathematics (STEM) programs at a South African university. Employing a Bayesian hierarchical modelling approach, this research analyses data from 630 students collected over four academic years (2019–2023). The findings indicate that high school mathematics marks and progression rates serve as significant predictors of academic success, confirming the critical role of foundational mathematical skills in enhancing university performance. Interestingly, gender and age were found to have no statistically significant impact on academic outcomes, suggesting that these factors may be less influential in this context. Additionally, socio-economic status, represented by school quintiles, emerged as a substantial determinant of performance, highlighting disparities faced by students from disadvantaged backgrounds. The results underscore the necessity for targeted educational interventions aimed at bolstering the academic capabilities of students entering university, particularly those with weaker mathematics backgrounds. Furthermore, the study advocates for a holistic admissions approach that considers various attributes beyond standardized scores. These insights contribute to the existing literature on STEM education and provide practical recommendations for educators and policymakers aiming to foster equitable academic success among all students

Bayesian probability · Context effect · Machine learning · Mathematics education · Multilevel model · Computer Science · Education, Achievement, and Giftedness · Global Educational Reforms and Inequalities · Mathematics · Psychology · School Choice and Performance · Artificial Intelligence

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