IDEAS home Printed from https://ideas.repec.org/a/eee/intell/v77y2019ics0160289619301801.html
   My bibliography  Save this article

Estimating the true extent of gender differences in scholastic achievement: A neural network approach

Author

Listed:
  • Loesche, Philipp Manuel

Abstract

In this study neural networks are employed to analyze individual item scores in a large-scale achievement test. They are able to correctly identify the participant's gender in 65.1% of cases, performing much better than a competing model based on differences in subject domain performances. It follows that substantial gender-related information is contained in the items, and comparisons based on performance can only provide a limited view of gender differences in scholastic achievement. An exploratory view of what the networks learn is presented and perspectives for further research are discussed.

Suggested Citation

  • Loesche, Philipp Manuel, 2019. "Estimating the true extent of gender differences in scholastic achievement: A neural network approach," Intelligence, Elsevier, vol. 77(C).
    • Handle: RePEc:eee:intell:v:77:y:2019:i:c:s0160289619301801
    DOI: 10.1016/j.intell.2019.101398
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160289619301801
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.intell.2019.101398?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gijsbert Stoet & David C Geary, 2013. "Sex Differences in Mathematics and Reading Achievement Are Inversely Related: Within- and Across-Nation Assessment of 10 Years of PISA Data," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-10, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huong Thu Le & Ha Trong Nguyen, 2018. "The evolution of the gender test score gap through seventh grade: new insights from Australia using unconditional quantile regression and decomposition," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 7(1), pages 1-42, December.
    2. Giofrè, D. & Allen, K. & Toffalini, E. & Mammarella, I.C. & Caviola, S., 2022. "Decoding gender differences: Intellectual profiles of children with specific learning disabilities," Intelligence, Elsevier, vol. 90(C).
    3. Shulamit Kahn & Donna Ginther, 2017. "Women and STEM," NBER Working Papers 23525, National Bureau of Economic Research, Inc.
    4. Maarten Jan Wensink & Linda Juel Ahrenfeldt & Sören Möller, 2020. "Variability Matters," IJERPH, MDPI, vol. 18(1), pages 1-8, December.
    5. Afkar,Rythia & Yarrow,Noah Bunce & Surbakti,Soedarti & Cooper,Rachel Danielle, 2020. "Inclusion in Indonesia's Education Sector : A Subnational Review of Gender Gaps and Children with Disabilities," Policy Research Working Paper Series 9282, The World Bank.
    6. Markus Daniel Meier, 2021. "Ars docendi," Books, Universidad Externado de Colombia, Facultad de Derecho, number 1325, August.
    7. Giannelli, Gianna Claudia & Rapallini, Chiara, 2016. "Immigrant student performance in Math: Does it matter where you come from?," Economics of Education Review, Elsevier, vol. 52(C), pages 291-304.
    8. M. E Letsoalo, 2018. "The Effect of Mathematics and Physical Science on Matriculants’ Overall Performances: Analysis Using Multilevel Model," Journal of Education and Vocational Research, AMH International, vol. 8(4), pages 28-42.
    9. Munir, Farzana & Winter-Ebmer, Rudolf, 2018. "Decomposing international gender test score differences," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 52(1), pages 1-12.
    10. Ülle Säälik, 2015. "Reading performance, learning strategies, gender and school language as related issues ? PISA 2009 findings in Finland and Estonia," International Journal of Teaching and Education, International Institute of Social and Economic Sciences, vol. 3(2), pages 17-30, June.
    11. Recep BİNDAK, 0. "Lojistik Regresyon Analizi ile Pisa Araştırmasında Öğrenci Başarısının Modellenmesi," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 14(28), pages 57-74.
    12. Anne (A.C.) Gielen & Esmee Zwiers, 2018. "Biology and the gender gap in educational performance - The role of prenatal testosterone in test scores," Tinbergen Institute Discussion Papers 18-086/V, Tinbergen Institute.
    13. Nguyen, Ha, 2015. "The evolution of the gender test score gap through seventh grade: New insights from Australia using quantile regression and decomposition," MPRA Paper 67586, University Library of Munich, Germany.
    14. José A. Robles-Zurita, 2017. "Cognitive skills and the LOGSE reform in Spain: evidence from PIAAC," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 8(4), pages 401-415, November.
    15. Daniel Fellman & Richard Bränström & Agneta Herlitz, 2021. "Revisiting a basic question: does growing up in either female or male environment affect sex differences in academic strengths and occupational choices?," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-10, December.
    16. Saulius Sukys & Laima Trinkuniene & Ilona Tilindiene, 2019. "Subjective Health Literacy among School-Aged Children: First Evidence from Lithuania," IJERPH, MDPI, vol. 16(18), pages 1-11, September.
    17. Warren Sanderson & Sergei Scherbov, 2016. "A unifying framework for the study of population aging," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 14(1), pages 007-40.
    18. Oberleiter, Sandra & Fries, Jonathan & Schock, Laura S. & Steininger, Benedikt & Pietschnig, Jakob, 2023. "Predicting cross-national sex differences in large-scale assessments of students' reading literacy, mathematics, and science achievement: Evidence from PIRLS and TIMSS," Intelligence, Elsevier, vol. 100(C).
    19. Reynolds, Matthew R. & Hajovsky, Daniel B. & Caemmerer, Jacqueline M., 2022. "The sexes do not differ in general intelligence, but they do in some specifics," Intelligence, Elsevier, vol. 92(C).
    20. Dekhtyar, S. & Weber, D. & Helgertz, J. & Herlitz, A., 2018. "Sex differences in academic strengths contribute to gender segregation in education and occupation: A longitudinal examination of 167,776 individuals," Intelligence, Elsevier, vol. 67(C), pages 84-92.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intell:v:77:y:2019:i:c:s0160289619301801. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/intelligence .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.