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

Abstract

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.

Keywords

• Finansal okuryazarlık
• ekonomi
• makine öğrenme
• PISA 2022

Ethical Statement

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

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.

Keywords

• Financial literacy
• machine learning
• PISA 2022
• economy

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