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

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  • 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
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    References listed on IDEAS

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    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.
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