The Bayesian Model Averaging (BMA) Approach for Determining the Factors Affecting the Achievement of Students with Low Socioeconomic Status

Year 2024, , 1 - 11, 26.06.2024

Derya Topdağ , Ebru Çağlayan Akay

https://doi.org/10.26650/ekoist.2024.40.1254248

Abstract

Analyzing the relationship between academic achievement and socioeconomic background is an important subject in educational research. Even though the percentage of students with low socioeconomic status in Türkiye is higher than the international average, these students’ average mathematics achievement scores can be shown to relatively higher than international average scores. This study aims to identify the variables that influence the mathematics achievement of students with low socioeconomic status in Türkiye using the small sample size and modeling flexibility provided by the Bayesian approach. Data were employed for this purpose from the 2019 International Survey of Mathematics and Science Trends (TIMSS) 8th-grade mathematics assessment. The study uses the Bayesian model averaging (BMA) approach to determine which variables should be included in the model when working with large-scale educational data and a large number of independent variables. According to the Bayesian model averaging results, the number of books at home, students’ academic expectations, sense of belonging to school, attitudes toward mathematics, absenteeism, and exposure to bullying are the strongest predictors of mathematics achievement. The findings from this study show the mathematics failure of students with low socioeconomic status to be closely associated with negative attitudes toward school and mathematics courses, exposure to bullying, and greater frequency of homework. Furthermore, the study has determined mother’s educational level to have no influence on the mathematics achievement of students with low socioeconomic status, while gender does have an effect in terms of father’s education level. The results show students with low socioeconomic status to be impacted by the components of inequalities inside and outside of school. Consequently, education policies are expected to provide equitable opportunities for students with low socioeconomic status by taking socioeconomic inequalities into account.

Keywords

Bayesian model averaging, BMA, TIMSS, mathematics achievement, low socioeconomic status

Düşük Sosyoekonomik Statüye Sahip Öğrencilerin Başarısını Etkileyen Faktörlerin Belirlenmesi: Bayesyen Model Ortalama Yaklaşımı

Abstract

Akademik başarı ve sosyoekonomik arka plan arasındaki ilişkinin analizi, eğitim araştırmalarında önemli konulardan biridir. Türkiye’de düşük sosyoekonomik statüye sahip öğrenci yüzdesinin uluslararası ortalamanın üstünde olmasına rağmen, bu öğrencilerin özellikle ortalama matematik başarı puanlarının uluslararası ortalama puanına göre nispeten yüksek olduğu görülmektedir. Bu makalenin amacı, Türkiye’de düşük sosyoekonomik statüye sahip öğrencilerin matematik başarısını etkileyen değişkenleri Bayesyen yaklaşımın sunduğu küçük örneklem boyutu ve modelleme esnekliğinden yararlanarak belirlemektir. Çalışmanın verileri Uluslararası Matematik ve Fen Eğilimleri Araştırması (TIMSS) 2019 sekizinci sınıf matematik değerlendirmesinden elde edilmiştir. Çalışmada, çok sayıda bağımsız değişken içeren büyük ölçekli eğitim verileriyle çalışırken hangi değişkenlerin modele dahil edilmesi gerektiğini belirlemek için Bayesyen model ortalama (BMA) yaklaşımı kullanılmıştır. Bayesyen model ortalama sonuçlarına göre, evdeki kitap sayısı, öğrencinin akademik beklentisi, okula ait hissetme, matematiğe karşı tutum, devamsızlık ve zorbalığa maruz kalma, matematik performansının en önemli açıklayıcıları olarak tespit edilmiştir. Düşük sosyoekonomik statüye sahip öğrencilerin matematik başarısızlığının okula ve matematik dersine karşı olumsuz tutumlar, zorbalığa maruz kalma ve artan ödev sıklığı ile yakından ilişkili olduğu belirlenmiştir. Ayrıca düşük sosyoekonomik statüye sahip öğrencilerin matematik başarısında annenin eğitim seviyesi ve cinsiyetin etkisi olmadığı tespit edilmiştir. Sonuçlar düşük sosyoekonomik statüye sahip öğrencilerin okul içinde ve okul dışındaki eşitsizlik unsurlarından etkilendiğini göstermektedir. Sonuç olarak, eğitim politikalarının sosyoekonomik eşitsizlikleri dikkate alarak düşük sosyoekonomik statüye sahip öğrenciler için fırsat eşitliği sunması beklenmektedir.

Keywords

Bayesyen Model Ortalama (BMA), TIMSS, Matematik Başarısı, Düşük Sosyoekonomik Statü (low-SES)

References

• APA. (2017). Education and Socioeconomic Status. https://www.apa.org/pi/ses/resources/publications/education google scholar
• Agasisti, T., Avvisati, F., Borgonovi, F., & Longobardi, S. (2021). What school factors are associated with the success of socio-economically dis-advantaged students? An empirical investigation using PISA data. Social Indicators Research, 157, 749-781. https://doi.org/10.1007/s11205-021-02668-w google scholar
• Akyüz G. (2014). The effects of student and school factors on mathematics achievement in TIMSS 2011. Education and Science, 39(172), 150-162 google scholar
• Broer, M., Bai, Y., & Fonseca, F. (2019). Methodology: Constructing a socioeconomic index for TIMSS trend analyses. In S.Hegarty & L. Rutkowski (Eds.), Socioeconomic Inequality and Educational Outcomes. IEA Research for Education, Springer. https://doi.org/10.1007/978-3-030-11991-1_3 google scholar
• Bielinski, J., & Davison, M. L. (2001). A sex difference by item difficulty interaction in multiple-choice mathematics items administered to national probability samples. Journal of Educational Measurement, 38, 51-77. google scholar
• Coleman, J.S., Campbell, E.Q., Hobson, C.J., McPartland, J., Mood, A.M., Weinfeld, F.D. & York, R.L. (1966). Equality of educational opportunity. Washington, DC: US Government Printing Office. google scholar
• Erberber, E., Stephens, M., Mamedova, S., Ferguson, S., & Kroeger, T. (2015). Socioeconomically disadvantaged students who are academically successful: Examining academic resilience cross-nationally. IEA’s Policy Brief Series, No.5. google scholar
• Ermisch, J., & Pronzato, C. (2010). Causal effects of parents’ education on children’s education. ISER Working Paper Series, (No. 2010-16). google scholar
• Fındık, L. Y., ve Kavak, Y. (2013). Türkiye’deki sosyoekonomik açıdan dezavantajlı öğrencilerin PISA 2009 başarılarının değerlendirilmesi. Educational Administration: Theory and Practice, 19(2), 249-273. google scholar
• Hernandez, A. S., & Cortes, D. (2012, January). Factors and conditions that promote academic resilience: A cross-country perspective. Conference: International Congress for School Effectiveness Improvement (ICSEI). Malmö, Sweden. google scholar
• Hernandez, A., & Bialowolski, P. (2016). Factors and conditions promoting academic resilience: a TIMSS-based analysis of five Asian education systems. Asia Pacific Education Review, 17(3), 511-520. google scholar
• Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical science, 14(4), 382-417. google scholar
• Hoeting, J. A., Raftery, A. E., & Madigan, D. (2002). Bayesian variable and transformation selection in linear regression. Journal of Computational and Graphical Statistics, 11(3), 485-507. google scholar
• Kaplan, D., & Lee, C. (2018). Optimizing prediction using Bayesian model averaging: Examples using large-scale educational assessments. Evaluation review, 42(4), 423-457. google scholar
• Kalender, İ. (2015). An Analysis of the Profile of Resilient Students based on PISA 2012. Journal of Measurement and Evaluation in Education and Psychology, 6(1), 158-172. https://doi.org/10.21031/epod.16925 google scholar
• Kelly, D.L., Centurino, V.A.S., Martin, M.O., & Mullis, I.V.S. (Eds.). (2020). TIMSS 2019 Encyclopedia: Education Policy and Curriculum in Mathematics and Science. Retrieved from Boston College, TIMSS & PIRLS International Study Center website: https://timssandpirls.bc.edu/timss2019/encyclopedia/ google scholar
• Kitchen, H., Bethell G., Fordham E., Henderson K. & Li R. (2019). OECD reviews of evaluation and assessment in education: Student assessment in Turkey, OECD Reviews of Evaluation and Assessment in Education, OECD Publishing, Paris, https://doi.org/10.1787/5edc0abe-en google scholar
• König, C., & van de Schoot, R. (2018). Bayesian statistics in educational research: a look at the current state of affairs. Educational Review, 70(4), 486-509. google scholar
• Leamer, E. E. (1978). Specification Searches. New York, Wiley. google scholar
• Madigan, D., & Raftery, A. E. (1994). Model selection and accounting for model uncertainty in graphical models using Occam’s window. Journal of the American Statistical Association, 89(428), 1535-1546. google scholar
• Martin, M. O., Von Davier, M., & Mullis, I. V. (Eds.). (2020). Methods and procedures: TIMSS 2019 technical report. https://timssandpirls.bc.edu/timss2019/methods/pdf/TIMSS-2019-MP-Technical-Report.pdf google scholar
• OECD (2010). PISA 2009 results: Overcoming social background: Equity in learning opportunities and outcomes (Volume II). OECD Publishing, Paris. http://dx.doi.org/10.1787/9789264091504-en google scholar
• OECD (2011). Against the odds: Disadvantaged students who succeed in school, OECD Publishing. http://dx.doi.org/10.1787/9789264090873-en google scholar
• OECD (2016). PISA 2015 results (Volume II): Policies and practices for successful schools, PISA, OECD Publishing, Paris. http://dx.doi.org/10.1787/9789264267510-en google scholar
• OECD (2019). PISA 2018 results (Volume II): Where all students can succeed, PISA, OECD Publishing, Paris, https://doi.org/10.1787/b5fd1b8f-en google scholar
• Önder, E. & Uyar, Ş. (2018). Factors affecting the academic achievement in socioeconomically disadvantaged students. Pegem Journal of Education and Instruction, 8(2), 253-280, http://dx.doi.org/10.14527/pegegog.2018.011 google scholar
• Raftery, A. E. (1995). Bayesian model selection in social research. Sociological methodology, 111-163. google scholar
• Raftery, A. E., Madigan, D., & Hoeting, J. A. (1997). Bayesian model averaging for linear regression models. Journal of the American Statistical Association, 92(437), 179-191. google scholar
• Raftery, A. E. (1998). Bayes factors and BIC: Comment on Weakliem (No. 347). Tech. Rep. google scholar
• Raftery, A., Hoeting, J., Volinsky, C., Painter, I., Yeung, K. Y., Sevcikova, M. H., & Suggests, M. A. S. S. (2015). Package BMA. Tech. Rep. google scholar
• Rodriguez, M. C. (2004). The Role of classroom assessment in student performance on TIMSS. Applied Measurement in Education, 17 (1), 1-24. google scholar
• Sandoval-Hernandez, A., & Bialowolski, P. (2016). Factors and conditions promoting academic resilience: a TIMSS-based analysis of five Asian education systems. Asia Pacific Education Review, 17(3), 511-520. google scholar
• Schmidt, W. H., Burroughs, N. A., Zoido, P., & Houang, R. T. (2015). The role of schooling in perpetuating educational inequality: An international perspective. Educational researcher, 44(7), 371-386. https://doi.org/10.3102/0013189X15603982 google scholar
• Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of educational research, 75(3), 417-453. google scholar
• TIMSS (2020). TIMSS 2019 International Results in Mathematics and Science. Retrieved from Boston College, TIMSS & PIRLS International Study Center website: https://timssandpirls.bc.edu/timss2019/international-results/ google scholar
• Topal, H. (2021). Variable selection via the adaptive elastic net: mathematics success of the students in Singapore and Turkey. Journal of Applied Microeconometrics, 1(1), 41-55. google scholar
• Topçu, M.S., Erbilgin, E. & Arıkan, S. (2016). Factors predicting Turkish and Korean students’ science and mathematics achievement in TIMSS 2011. Eurasia Journal of Mathematics, Science & Technology Education, 12(7), 1711-1737. google scholar
• UNDP (2019). Human Development Report 2019: Beyond income, beyond averages, beyond today: Inequalities in human development in the 21st century. New York. https://hdr.undp.org/content/human-development-report-2019 google scholar
• Viallefont, V., Raftery, A. E., & Richardson, S. (2001). Variable selection and Bayesian model averaging in case-control studies. Statistics in Medicine, 20(21), 3215-3230. google scholar
• Zeugner, S., & Feldkircher, M. (2015). Bayesian model averaging employing fixed and flexible priors: The BMS package for R. Journal of Statistical Software, 68, 1-37. google scholar
• Wang, M. C., Haertel, G. D., & Walberg, H.J. (1998). Educational Resilience. Fastback 43. https://doi.org/10.1007/978-0-387-71799-9_155 google scholar
• Willms, J. & L. Tramonte (2015). Towards the development of contextual questionnaires for the PISA for development study. OECD Education Working Papers, No. 118, OECD Publishing, Paris, https://dx.doi.org/10.1787/5js1kv8crsjf-en google scholar