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

Selin Erdoğan and Hüseyin Taştan
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Selin Erdoğan: Department of Economics, Yildiz Technical University, İstanbul, Türkiye
Hüseyin Taştan: Department of Economics, Yildiz Technical University, İstanbul, Türkiye

Yildiz Social Science Review, 2024, vol. 10, issue 1, 7-27

Abstract: This study seeks to identify the determinants of academic performance in mathematics, sci-ence, 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 regres-sion trees. Notably, the BRT highlights the importance of general secondary education pro-grams 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 con-tribute to the development of evidence-based educational policies in Turkey.

Keywords: Economics of education; educational data mining; school effectiveness; student achievement; machine learningJournal: Yildiz Social Science Review (search for similar items in EconPapers)
JEL-codes: F00 F30 G00 G10 K00 K20 M00 M20 O10 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:aye:journl:v:10:y:2024:i:1:p:7-27

DOI: 10.51803/yssr.1461030

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