Predicting Student Achievement via Machine Learning: Evidence from Turkish Subset of PISA

Authors : Selin Erdoğan, Hüseyin Taştan

Pages : 7-27

Doi:10.51803/yssr.1461030

View : 71 | Download : 116

Publication Date : 2024-06-28

Article Type : Research Paper

Abstract :This study seeks to identify the determinants of academic performance in mathematics, science, and reading among Turkish secondary school students. Using data from the OECD\'s PISA 2018 survey, which includes several student- and school-level variables as well as test scores, we employed a range of supervised machine learning methods specifically ensemble decision trees to assess their predictive performance. Our results indicate that the boosted regression tree (BRT) method outperforms other methods bagging and random forest regression trees. Notably, the BRT highlights the importance of general secondary education programs over vocational and technical (VAT) education in predicting academic achievement. Moreover, both characteristics specific to student and school environment are demonstrated to be significant predictors of academic performance in all subject areas. These findings contribute to the development of evidence-based educational policies in Turkey.
Keywords : Eğitim Ekonomisi, Makine Öğrenmesi, Öğrenci Başarısı, Eğitimsel Veri Madenciliği