An Analysis of Factors Influencing Science Literacy in PISA 2022 Using Bayesian Hierarchical Models and Graph Neural Networks

Abstract

in this study, using PISA 2022 science literacy data, individual, family, and school-level factors influencing students’ science achievement were examined using a multi-method approach. A multiple imputation approach combined with Rubin’s rules was used to address missing data; subsequently, classical regression, a Bayesian hierarchical regression model, and a GraphSAGE-based Graph Neural Network (GNN) model were applied. The findings indicate that the different methods produced largely consistent results regarding the key variables explaining science achievement. Educational and Cultural Resources at Home, the Socioeconomic Status Index, Parental Occupational Prestige, and Strategic Learning Habits were identified as the strongest positive predictors of science achievement in all analyses. In contrast, total weekly course load, weekly out-of-school science study time, and some indicators of instructional support were found to have low-level negative or insignificant effects. Bayesian analyses, while highlighting the uncertainty of regression coefficients through confidence intervals, largely supported the results of the multiple assignment regression. The GraphSAGE-based model, taking into account the relational structure among students, explained approximately 37% of the variance in science achievement in the test data and demonstrated that the model converged stably. In conclusion, while the study highlights the decisive role of family resources and socioeconomic indicators in explaining science achievement, it also demonstrates that Graph Neural Networks and Bayesian statistical approaches offer complementary and powerful methods for analyzing large-scale educational data.

Keywords

• PISA 2022
• Science literacy
• Bayesian hierarchical regression
• Graph neural network
• Multiple assignment.

Ethical Statement

Bu çalışmada, “Yükseköğretim Kurumları Bilimsel Araştırma ve Yayın Etiği Yönergesi” kapsamında uyulması gerekli tüm kurallara uyulduğunu, bahsi geçen yönergenin “Bilimsel Araştırma ve Yayın Etiğine Aykırı Eylemler” başlığı altında belirtilen eylemlerden hiçbirinin gerçekleştirilmediğini taahhüt ederiz.

PISA 2022 Fen Okuryazarlığını Etkileyen Faktörlerin Bayesyen Hiyerarşik Model ve Grafik Sinir Ağları ile İncelenmesi

Abstract

Bu çalışmada, PISA 2022 fen okuryazarlığı verileri kullanılarak öğrencilerin fen başarısını etkileyen bireysel, ailesel ve okul düzeyindeki faktörler çok yöntemli bir yaklaşımla incelenmiştir. Eksik verilerin giderilmesinde Rubin kuralları ile birleştirilen çoklu atama yaklaşımı kullanıldıktan sonra klasik regresyon, Bayesyen hiyerarşik regresyon modeli ve GraphSAGE tabanlı Grafik Sinir Ağı modeli uygulanmıştır. Bulgular, farklı yöntemlerin fen başarısını açıklayan temel değişkenler açısından büyük ölçüde tutarlı sonuçlar ürettiğini göstermektedir. Evdeki Eğitsel ve Kültürel Kaynaklar, Sosyoekonomik Statü İndeksi, Ebeveyn Mesleki Prestiji ve Stratejik Öğrenme Alışkanlıkları tüm analizlerde fen başarısının en güçlü pozitif yordayıcıları olarak belirlenmiştir. Buna karşılık toplam haftalık ders yükü, okul dışı haftalık fen çalışma süresi ve bazı öğretim desteği göstergelerinin düşük düzeyde negatif veya anlamsız etkiler gösterdiği belirlenmiştir. Bayesyen analizler, regresyon katsayılarının belirsizliğini güvenilirlik aralıklarıyla ortaya koyarken, çoklu atama regresyon sonuçlarını büyük ölçüde desteklemiştir. GraphSAGE tabanlı model ise öğrenciler arasındaki ilişkisel yapıyı dikkate alarak test verisinde fen başarısındaki varyansın yaklaşık %37'sini açıklamış ve modelin kararlı biçimde yakınsadığını göstermiştir. Sonuç olarak çalışma, fen başarısının açıklanmasında aile kaynakları ve sosyoekonomik göstergelerin belirleyici rolünü ortaya koyarken, grafik sinir ağları ile Bayesyen istatistiksel yaklaşımların büyük ölçekli eğitim verilerinin analizinde tamamlayıcı ve güçlü yöntemler sunduğunu göstermektedir.

Keywords

• PISA 2022
• Fen okuryazarlığı
• Bayesyen hiyerarşik regresyon
• Grafik sinir ağı
• Çoklu atama

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