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

Author

Listed:
  • Selin ERDOĞAN

    (Department of Economics, Yildiz Technical University, İstanbul, Türkiye)

  • Hüseyin TAŞTAN

    (Department of Economics, Yildiz Technical University, İstanbul, Türkiye)

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.

Suggested Citation

  • Selin ERDOĞAN & Hüseyin TAŞTAN, 2024. "Predicting Student Achievement via Machine Learning: Evidence from Turkish Subset of PISA," Yildiz Social Science Review, Yildiz Technical University, vol. 10(1), pages 7-27.
    • Handle: RePEc:aye:journl:v:10:y:2024:i:1:p:7-27
    DOI: 10.51803/yssr.1461030
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    References listed on IDEAS

    as
    1. Rebai, Sonia & Ben Yahia, Fatma & Essid, Hédi, 2020. "A graphically based machine learning approach to predict secondary schools performance in Tunisia," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    2. Masci, Chiara & Johnes, Geraint & Agasisti, Tommaso, 2018. "Student and school performance across countries: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1072-1085.
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    JEL classification:

    • F00 - International Economics - - General - - - General
    • F30 - International Economics - - International Finance - - - General
    • G00 - Financial Economics - - General - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • K00 - Law and Economics - - General - - - General (including Data Sources and Description)
    • K20 - Law and Economics - - Regulation and Business Law - - - General
    • M00 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General - - - General
    • M20 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - General
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General

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