Bayesian Hierarchical Modelling of Student Academic Performance: The Impact of Mathematics Competency, Institutional Context, and Temporal Variability
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 fr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Education Sciences |
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| Online Access: | https://www.mdpi.com/2227-7102/15/2/177 |
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| author | Moeketsi Mosia Felix O. Egara Fadip A. Nannim Moses Basitere |
| author_facet | Moeketsi Mosia Felix O. Egara Fadip A. Nannim Moses Basitere |
| author_sort | Moeketsi Mosia |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a1bc0153d7264774a512be2239f87f2e |
| institution | DOAJ |
| issn | 2227-7102 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj-art-a1bc0153d7264774a512be2239f87f2e2025-08-20T03:11:21ZengMDPI AGEducation Sciences2227-71022025-02-0115217710.3390/educsci15020177Bayesian Hierarchical Modelling of Student Academic Performance: The Impact of Mathematics Competency, Institutional Context, and Temporal VariabilityMoeketsi Mosia0Felix O. Egara1Fadip A. Nannim2Moses Basitere3Department of Mathematics, Natural Sciences and Technology Education, Faculty of Education University of the Free State, Bloemfontein 9301, South AfricaDepartment of Mathematics, Natural Sciences and Technology Education, Faculty of Education University of the Free State, Bloemfontein 9301, South AfricaDepartment of Mathematics, Natural Sciences and Technology Education, Faculty of Education University of the Free State, Bloemfontein 9301, South AfricaAcademic Support for Engineering in Cape Town (ASPECT), Centre for Higher Education Development, Upper Campus, University of Cape Town, Cape Town 7701, South AfricaThis 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.https://www.mdpi.com/2227-7102/15/2/177academic performanceBayesian hierarchical modellingeducational interventionsgender differencessocio-economic statusSTEM education |
| spellingShingle | Moeketsi Mosia Felix O. Egara Fadip A. Nannim Moses Basitere Bayesian Hierarchical Modelling of Student Academic Performance: The Impact of Mathematics Competency, Institutional Context, and Temporal Variability Education Sciences academic performance Bayesian hierarchical modelling educational interventions gender differences socio-economic status STEM education |
| title | Bayesian Hierarchical Modelling of Student Academic Performance: The Impact of Mathematics Competency, Institutional Context, and Temporal Variability |
| title_full | Bayesian Hierarchical Modelling of Student Academic Performance: The Impact of Mathematics Competency, Institutional Context, and Temporal Variability |
| title_fullStr | Bayesian Hierarchical Modelling of Student Academic Performance: The Impact of Mathematics Competency, Institutional Context, and Temporal Variability |
| title_full_unstemmed | Bayesian Hierarchical Modelling of Student Academic Performance: The Impact of Mathematics Competency, Institutional Context, and Temporal Variability |
| title_short | Bayesian Hierarchical Modelling of Student Academic Performance: The Impact of Mathematics Competency, Institutional Context, and Temporal Variability |
| title_sort | bayesian hierarchical modelling of student academic performance the impact of mathematics competency institutional context and temporal variability |
| topic | academic performance Bayesian hierarchical modelling educational interventions gender differences socio-economic status STEM education |
| url | https://www.mdpi.com/2227-7102/15/2/177 |
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