KaydedilenlerDerginizi ekleyin

Dergiler / Sosyal Politika Çalışmaları Dergisi / 2026 / Cilt: 25 Sayı: 68

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

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

Ayça Akın(Antalya Belek Üniversitesi)0000-0002-6107-3487
Emine Ebru Bozçelik(Antalya Belek Üniversitesi)0000-0001-6491-0140
Sayfa
649–683
Yayın Tarihi
2025-09-30
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez: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.
📊
Temel Bulgular & Veri:Ülkeler bazında farklı algoritmaların farklı başarılar göstermesi dikkat çekicidir.
💡
Sonuç & Katkı: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.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2022 Verileri
⚙️Değişkenler:Finansal okuryazarlık · ekonomi · makine öğrenme

Özet

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.

Abstract

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.

🔖

Bu Makaleye Atıf Yap

Akın, A., Bozçelik, E. E. (2025). FİNANSAL OKURYAZARLIK PERFORMANSINI TAHMİN ETMEDE MAKİNE ÖĞRENMESİ ALGORİTMALARININ ROLÜ: DANİMARKA, ABD VE MALEZYA KARŞILAŞTIRMASI. Sosyal Politika Çalışmaları Dergisi, Cilt: 25 Sayı: 68, 649–683. https://doi.org/10.21560/spcd.vi.1675398

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (66)

  1. 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
  2. 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
  3. Angrist, J. D., and Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton University Press.
  4. 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
  5. Atkinson, A., and Messy, F. (2012). Measuring financial literacy: Results of the OECD/INFE pilot study. OECD Publishing.
  6. 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
  7. Bank Negara Malaysia. (2021). Financial stability review - Second half 2021. Kuala Lumpur: Bank Negara Malaysia.
  8. Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman and Company.
  9. 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.
  10. Bergstra, J., and Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(1), 281–305.
  11. 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.
  12. 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.
  13. Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
  14. 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.
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. Eurostat. (2022). Living conditions in Europe - 2022 edition. Publications Office of the European Union.
  21. 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
  22. 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
  23. 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
  24. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. Cambrigde: MIT Press.
  25. 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.
  26. 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
  27. 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
  28. 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
  29. 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.
  30. 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.
  31. Kutner, M. H., Nachtsheim, C. J., Neter, J., and Li, W. (2005). Applied linear statistical models. McGraw-Hill/Irwin.
  32. 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
  33. Lundberg, S. M., and Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4768-4777.
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. Molnar, C. (2019). Interpretable machine learning: A guide for making black box models explainable. Leanpub.
  41. 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.
  42. 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
  43. Organisation for Economic Co-operation and Development (OECD). (2023a). OECD/INFE 2023 international survey of adult financial literacy. OECD Publishing.
  44. Organisation for Economic Co-operation and Development (OECD). (2023b). PISA 2022 assessment and analytical framework. OECD Publishing.
  45. Organisation for Economic Co-operation and Development (OECD). (2023c). PISA 2022 results (Volume I): The state of learning and equity in education. OECD Publishing.
  46. 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
  47. Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge University Press.
  48. Piaget, J. (1977). The development of thought: Equilibration of cognitive structures. Viking Press.
  49. 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
  50. 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
  51. 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
  52. 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.
  53. 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.
  54. 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
  55. 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
  56. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. https://doi.org/10.1207/s15516709cog1202_4
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. 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
  63. 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
  64. 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.
  65. 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.
  66. 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