FİNANSAL OKURYAZARLIK PERFORMANSINI TAHMİN ETMEDE MAKİNE ÖĞRENMESİ ALGORİTMALARININ ROLÜ: DANİMARKA, ABD VE MALEZYA KARŞILAŞTIRMASI

Yıl 2025, Cilt: 25 Sayı: 68, 649 - 683, 30.09.2025

Ayça Akın , Emine Ebru Bozçelik

https://doi.org/10.21560/spcd.vi.1675398

Öz

Bu çalışma, PISA 2022 verilerini kullanarak Danimarka, ABD ve Malezya'daki öğrencilerin finansal okuryazarlık performansını tahmin etmede makine öğrenmesi algoritmalarının etkinliğini ve belirleyici faktörleri karşılaştırmayı amaçlamaktadır. Araştırmada, beş makine öğrenmesi algoritması (Rastgele Orman, Gradyan Artırma, Destek Vektör Makineleri, K-En Yakın Komşular ve Düzeltilmiş Lineer Regresyon) karşılaştırmalı olarak uygulanmış, topluluk öğrenme algoritmalarından özellikle Gradyan Artırma ve Rastgele Orman algoritmasının büyük veri setlerinde daha başarılı olduğu tespit edilmiştir. Ülkeler bazında farklı algoritmaların farklı başarılar göstermesi dikkat çekicidir. Matematik okuryazarlığı her üç ülkede de en güçlü yordayıcı olarak belirlenirken, okuma becerileri de önemli bir rol oynamıştır. Malezya'da sosyoekonomik faktörlerin daha belirleyici olduğu gözlemlenmiştir. Ayrıca, finansal konularda özgüvenin Danimarka ve ABD’de etkili olduğu bulunmuştur. Temel bilişsel becerilerin (matematik okuryazarlığı ve okuma becerileri) finansal okuryazarlıkla olan ilişkisi, eğitim stratejilerinde bu alanlara odaklanmanın önemini göstermektedir. Modeller, yüksek ve düşük performanslı öğrencilerin tahmininde zorluk yaşamış, bu da daha derin psikolojik ve kültürel faktörler ile bireysel farklılıklar ile ilgili değişkenlerin eklenmesi gerekliliğine işaret etmiştir. Çalışma, finansal okuryazarlık eğitiminde matematik okuryazarlığı ve okuma becerilerine odaklanan bütünleşik politikaların önemini vurgulamakta, aynı zamanda sosyoekonomik ve kültürel açıdan ülke bağlamına özgü stratejilerin geliştirilmesi gerektiğini önermektedir.

Anahtar Kelimeler

Finansal okuryazarlık , ekonomi , makine öğrenme , PISA 2022

Etik Beyan

Bu çalışmanın etiksel açıdan uygunluğu Antalya Belek Üniversitesi Bilimsel Araştırma ve Etik Kurulu tarafından onaylanmıştır.

THE ROLE OF MACHINE LEARNING ALGORITHMS IN PREDICTING FINANCIAL LITERACY PERFORMANCE: A COMPARISON OF DENMARK, THE USA AND MALAYSIA

Öz

This study aims to compare the effectiveness of machine learning algorithms and key determinants in predicting students' financial literacy performance in Denmark, the USA, and Malaysia using PISA 2022 data. Five machine learning algorithms—Random Forest, Gradient Boosting, Support Vector Machines, K-Nearest Neighbors, and Regularized Linear Regression—were applied and compared. Ensemble learning algorithms, particularly Gradient Boosting and Random Forest, demonstrated superior performance with large datasets. Notably, different algorithms showed varying levels of success across countries. Mathematical literacy emerged as the strongest predictor in all three countries, while reading skills also played a significant role. In Malaysia, socioeconomic factors were found to be more influential, whereas financial self-confidence had a notable impact in Denmark and the USA. The strong relationship between core cognitive skills (mathematical literacy and reading skills) and financial literacy underscores the importance of integrating these areas into educational strategies. However, the models faced challenges in accurately predicting the performance of both high- and low-achieving students, suggesting the need to incorporate deeper psychological, cultural, and individual difference variables. The study highlights the importance of holistic policies focusing on mathematical literacy and reading skills in financial education while recommending context-specific strategies tailored to socioeconomic and cultural factors.

Anahtar Kelimeler

Financial literacy , machine learning , PISA 2022 , economy

Kaynakça

• Amagir, A., Groot, W., Maassen van den Brink, H., and Wilschut, A. (2018). A review of financial-literacy education programs for children and adolescents. Citizenship, Social and Economics Education, 17(1), 56-80. https://doi.org/10.1177/2047173417719555
• Agasisti, T., and Longobardi, S. (2017). Equality of educational opportunities, schools' characteristics and resilient students: An empirical study of EU-15 countries using OECD-PISA 2009 data. Social Indicators Research, 134(3), 917-953. https://doi.org/10.1007/s11205-016-1464-5
• Angrist, J. D., and Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton University Press.
• Athey, S., and Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11, 685-725. https://doi.org/10.1146/annurev-economics-080217-053433
• Atkinson, A., and Messy, F. (2012). Measuring financial literacy: Results of the OECD/INFE pilot study. OECD Publishing.
• Baker, S., and Inventado, P. S. (2016). Educational data mining and learning analytics: Potentials and possibilities for online education. In G. Veletsianos (Ed.), Emergence and Innovation in Digital Learning (83–98). https://doi.org/10.15215/aupress/9781771991490.01
• Bank Negara Malaysia. (2021). Financial stability review - Second half 2021. Kuala Lumpur: Bank Negara Malaysia.
• Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman and Company.
• Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. National Bureau of Economic Research, University of Chicago Press.
• Bergstra, J., and Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(1), 281–305.
• Belkhir, M., Brouard, M., Brunk, K. H., Dalmoro, M., Ferreira, M. C., Figueiredo, B., ... and Smith, A. N. (2019). Isolation in globalizing academic fields: A collaborative autoethnography of early career researchers. Academy of Management Learning and Education, 18(2), 261-285.
• Bourdieu, P. (1986). The forms of capital. In J. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241-258). Greenwood.
• Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
• Bronfenbrenner, U., and Morris, P. A. (2006). The bioecological model of human development. In W. Damon, R. M. Lerner (Eds.), Handbook of child psychology: Theoretical models of human development (pp. 793-828). John Wiley and Sons.
• Carpena, F., Cole, S., Shapiro, J., and Zia, B. (2019). The ABCs of financial education: Experimental evidence on attitudes, behavior, and cognitive biases. Management Science, 65(1), 346-369. https://doi.org/10.1287/mnsc.2017.2819
• Carpena, F., and Zia, B. (2020). The causal mechanism of financial education: Evidence from mediation analysis. Journal of Economic Behavior and Organization, 177, 143-184. https://doi.org/10.1016/j.jebo.2020.05.001
• Chen, T., and Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794). ACM. https://doi.org/10.1145/2939672.2939785
• Chen, J., Zhou, X., Yao, J., and Tang, S. K. (2025). Application of machine learning in higher education to predict students’ performance, learning engagement and self-efficacy: A systematic literature review. Asian Education and Development Studies. https://doi.org/10.1108/AEDS-08-2024-0166
• Cordero, J. M., Gil-Izquierdo, M., and Pedraja-Chaparro, F. (2022). Financial education and student financial literacy: A cross-country analysis using PISA 2012 data. The Social Science Journal, 59(1), 15-33. https://doi.org/10.1016/j.soscij.2019.07.011
• Eurostat. (2022). Living conditions in Europe - 2022 edition. Publications Office of the European Union.
• Fernandes, D., Lynch Jr, J. G., and Netemeyer, R. G. (2014). Financial literacy, financial education, and downstream financial behaviors. Management Science, 60(8), 1861-1883. https://doi.org/10.1287/mnsc.2013.1849
• Frisancho, V. (2020). The impact of financial education for youth. Economics of Education Review, 78, 101918. https://doi.org/10.1016/j.econedurev.2019.101918
• Gerardi, K., Goette, L., and Meier, S. (2010). Financial literacy and subprime mortgage delinquency: Evidence from a survey matched to administrative data. Federal Reserve Bank of Atlanta Working Paper Series, No. 2010-10. https://doi.org/10.2139/ssrn.1600905
• Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. Cambrigde: MIT Press.
• Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction. New York: Springer.
• Huston, S. J. (2010). Measuring financial literacy. Journal of Consumer Affairs, 44(2), 296-316. https://doi.org/10.1111/j.1745-6606.2010.01170.x
• Kahneman, D., and Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. https://doi.org/10.2307/1914185
• Kaiser, T., and Menkhoff, L. (2020). Financial education in schools: A meta-analysis of experimental studies. Economics of Education Review, 78, 101930. https://doi.org/10.1016/j.econedurev.2019.101930
• Klapper, L., Lusardi, A., and Van Oudheusden, P. (2015). Financial literacy around the world: Insights from the Standard and Poor's ratings services global financial literacy survey. Global Financial Literacy Excellence Center.
• Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence (IJCAI), 14(2), 1137-1145.
• Kutner, M. H., Nachtsheim, C. J., Neter, J., and Li, W. (2005). Applied linear statistical models. McGraw-Hill/Irwin.
• Lind, T., Ahmed, A., Skagerlund, K., Strömbäck, C., Västfjäll, D., and Tinghög, G. (2020). Competence, confidence, and gender: The role of objective and subjective financial knowledge in household finance. Journal of Family and Economic Issues, 41, 626-638. https://doi.org/10.1007/s10834-020-09678-9
• Lundberg, S. M., and Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4768-4777.
• Lusardi, A. (2019). Financial literacy and the need for financial education: Evidence and implications. Swiss Journal of Economics and Statistics, 155(1), 1-8. https://doi.org/10.1186/s41937-019-0027-5
• Lusardi, A., Hasler, A., and Yakoboski, P. J. (2021). Building up financial literacy and financial resilience. Mind and Society, 20, 181-187. https://doi.org/10.1007/s11299-020-00246-0
• Lusardi, A., and Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52(1), 5-44. https://doi.org/10.1257/jel.52.1.5
• Maden, S., ve Kaya, M. (2023). Meslek lisesi öğrencilerinin finansal okuryazarlık becerilerinin web 2.0 araçlarıyla geliştirilmesi. Anadolu Dil ve Eğitim Dergisi, 1(2), 69-78. Doi: 10.5281/zenodo.10445724
• Mishra, D., Agarwal, N., Sharahiley, S., and Kandpal, V. (2024). Digital Financial Literacy and Its Impact on Financial Decision-Making of Women: Evidence from India. Journal of Risk and Financial Management, 17(10), 468. https://doi.org/10.3390/jrfm17100468
• Mitchell, O. S., and Lusardi, A. (2022). Financial literacy and financial behavior at older ages. Wharton Pension Research Council Working Paper, No 2022-01. https://ssrn.com/abstract=4006687
• Molnar, C. (2019). Interpretable machine learning: A guide for making black box models explainable. Leanpub.
• Molnar, C., König, G., Herbinger, J., Freiesleben, T., Dandl, S., Scholbeck, C. A., ... and Bischl, B. (2020). General pitfalls of model-agnostic interpretation methods for machine learning models. In International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers (pp. 39-68). Cham: Springer International Publishing.
• Niu, J., Xu, H., and Yu, J. (2025). Identifying multilevel factors on student mathematics performance for Singapore, Korea, Finland, and Denmark in PISA 2022: considering individualistic versus collectivistic cultures. Humanities and Social Sciences Communications, 12(1), 1-12. https://doi.org/10.1057/s41599-025-04466-y
• Organisation for Economic Co-operation and Development (OECD). (2023a). OECD/INFE 2023 international survey of adult financial literacy. OECD Publishing.
• Organisation for Economic Co-operation and Development (OECD). (2023b). PISA 2022 assessment and analytical framework. OECD Publishing.
• Organisation for Economic Co-operation and Development (OECD). (2023c). PISA 2022 results (Volume I): The state of learning and equity in education. OECD Publishing.
• Pan, Z., and Cutumisu, M. (2024). Using machine learning to predict UK and Japanese secondary students' life satisfaction in PISA 2018. British Journal of Educational Psychology, 94(2), 474-498. https://doi.org/10.1111/bjep.12657
• Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge University Press.
• Piaget, J. (1977). The development of thought: Equilibration of cognitive structures. Viking Press.
• Riitsalu, L., and Murakas, R. (2019). Subjective financial knowledge, prudent behaviour and income: The predictors of financial well-being in Estonia. International Journal of Bank Marketing, 37(4), 934-950. https://doi.org/10.1108/IJBM-03-2018-0071
• Romero, C., and Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355. https://doi.org/10.1002/widm.1355
• Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
• Rutkowski, L., Rutkowski, D., and Svetina Valdivia, D. (2022). Multistage test design considerations in international large-scale assessments of educational achievement. In T. Nilsen, A. Stancel-Piatak, J. E. Gustafsson (Eds.), International handbook of comparative largescale studies in education: Perspectives, methods and findings (pp. 1–19). Cham: Springer International Publishing.
• Saarela, M., and Kärkkäinen, T. (2015). Analysing student performance using sparse data of core bachelor courses. Journal of Educational Data Mining, 7(1), 3-32.
• Shim, S., Serido, J., Tang, C., and Card, N. (2015). Socialization processes and pathways to healthy financial development for emerging young adults. Journal of Applied Developmental Psychology, 38, 29-38. https://doi.org/10.1016/j.appdev.2015.01.002
• Skagerlund, K., Lind, T., Strömbäck, C., Tinghög, G., and Västfjäll, D. (2018). Financial literacy and the role of numeracy–How individuals' attitude and affinity with numbers influence financial literacy. Journal of Behavioral and Experimental Economics, 74, 18-25. https://doi.org/10.1016/j.socec.2018.03.004
• Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. https://doi.org/10.1207/s15516709cog1202_4
• Thaler, R. H., and Shefrin, H. M. (1981). An economic theory of self-control. Journal of Political Economy, 89(2), 392-406. https://doi.org/10.1086/260971
• Urban, C., Schmeiser, M., Collins, J. M., and Brown, A. (2020). The effects of high school personal financial education policies on financial behavior. Economics of Education Review, 78, 101786. https://doi.org/10.1016/j.econedurev.2018.03.006
• Varian, H. R. (2014). Big Data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3-28. https://doi.org/10.1257/jep.28.2.3
• White, I. R., Royston, P., and Wood, A. M. (2011). Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine, 30(4), 377-399. https://doi.org/10.1002/sim.4067
• Xu, L., and Zia, B. (2012). Financial literacy around the world: An overview of the evidence with practical suggestions for the way forward. World Bank Policy Research Working Paper, 6107. https://doi.org/10.1596/1813-9450-6107
• Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., ... and Li, Y. (2020). A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity, 2021, 8812542. https://doi.org/10.1155/2021/8812542
• Zulkhibri, M. (2018). The impact of monetary policy on Islamic bank financing: Bank-level evidence from Malaysia. Journal of Economics, Finance and Administrative Science, 23(46), 306-322. https://doi.org/10.1108/JEFAS-01-2018-0011