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The Determinants of Mathematics Achievement: A Gender Perspective Using Multilevel Random Forest

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  • Alice Bertoletti

    (Joint Research Center, European Commission, 41092 Seville, Spain
    The views expressed are purely those of the author and may not in any circumstances be regarded as stating an official position of the European Commission.
    These authors contributed equally to this work.)

  • Marta Cannistrà

    (Department of Management, Economics and Industrial Engineering, Politecnico di Milano, 20133 Milan, Italy
    These authors contributed equally to this work.)

  • Melisa Diaz Lema

    (Department of Management, Economics and Industrial Engineering, Politecnico di Milano, 20133 Milan, Italy
    These authors contributed equally to this work.)

  • Chiara Masci

    (Department of Mathematical Engineering, Politecnico di Milano, 20133 Milan, Italy
    These authors contributed equally to this work.)

  • Anna Mergoni

    (Faculty of Economics and Business, KU Leuven, 3000 Leuven, Belgium
    These authors contributed equally to this work.)

  • Lidia Rossi

    (Department of Management, Economics and Industrial Engineering, Politecnico di Milano, 20133 Milan, Italy
    These authors contributed equally to this work.)

  • Mara Soncin

    (Department of Management, Economics and Industrial Engineering, Politecnico di Milano, 20133 Milan, Italy
    These authors contributed equally to this work.)

Abstract

This paper investigates the determinants of mathematics performance by gender, exploiting a multilevel random forest approach. OECD PISA 2018 data from 28 European countries are employed to explore the performance of male and female students as a function of students’ family characteristics, their attitudes towards education, and class and school environment. Results show that the gender gap in favour of boys persists in most European countries. However, teacher and school practices like fostering student reading and creating a cooperative environment allow mitigating the influence of family background in countries without gender gap. Policy implications to foster performance equality are provided.

Suggested Citation

  • Alice Bertoletti & Marta Cannistrà & Melisa Diaz Lema & Chiara Masci & Anna Mergoni & Lidia Rossi & Mara Soncin, 2023. "The Determinants of Mathematics Achievement: A Gender Perspective Using Multilevel Random Forest," Economies, MDPI, vol. 11(2), pages 1-20, January.
    • Handle: RePEc:gam:jecomi:v:11:y:2023:i:2:p:32-:d:1038751
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    References listed on IDEAS

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