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Gender Gap Among High Achievers in Math and Implications for STEM Pipeline

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Abstract

This study examined a new form of pervasive gender inequality: the gender gap among high achievers in math and considered its implication for developing STEM talents. Using the cross-nation Programme for International Student Achievement (PISA) data from both 2003 and 2012, we examined the mathematics gender gap among 15-year-old high achievers across ten countries/regions. We showed a consistent male advantage among the top performers in mathematics. Follow-up regression analyses revealed that the gap was associated with some socio-demographic and schooling/attitudinal variables, even after controlling for the background variables. We argue that education communities should acknowledge and address this form of gender inequality, as it could have ramifications for the science, technology, engineering, and mathematics (STEM) education pipeline. Educators and society in general still face challenges in closing the gender gap among high achievers in math with an aim to develop a gender-balanced STEM talent pool.

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Notes

  1. As of this writing, PISA 2015 data have just become available. Because PISA 2015 assessment did not have mathematics as the main assessment, but had science as the main assessment instead, for the reasons explained in the text, we decided not to analyze 2015 PISA data for our study.

  2. The effect size is the standardized mean difference in math performance, as represented by Cohen’s d \(\left( {d = \frac{{Mean_{male} - Mean_{female} }}{{S_{pooled} }}} \right)\). Five effect sizes were calculated based on five plausible values, and then we took the average of these five.

  3. For simplicity, we did not present inferential statistics of the ratio estimates in the figure. We did compute the 95% confidence interval for all the ratio estimates. The method and results are enclosed in the Appendix. Nine out of ten countries/regions have statistically significant male-to-female ratios higher than 1, with the United States being the exception.

  4. Proficiency level is OECD’s way of categorizing student performance. Proficiency level ranges from the lowest level 1 to highest level 6. Only 3.3 percent of OECD students achieved level 6 in mathematics in 2012. For details, see OECD (2014a, p. 61).

  5. Detailed information about the ORs of the background variables is available upon request from the corresponding author.

  6. To account for design effect of complex sampling, we applied statistical weights at both the student and school level. The weights were scaled using a method proposed by Pfeffermann et al. (1998).

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Acknowledgement

Yisu would like to thank Wei Li from University of Alabama for technical assistance and Zhongzhou Chen from Central Florida University for comments on an early draft.This work is supported by University of Macau under Grant MYRG2015-00052-FED.

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Authors and Affiliations

  1. Faculty of Education (E33), University of Macau, Room 3022, Av. da Universidade, Taipa, Macau SAR, China

    Yisu Zhou & Xitao Fan

  2. American Institute for Research, Washington, DC, USA

    Xiaoxin Wei

  3. University of Virginia, Charlottesville, VA, USA

    Robert H. Tai

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  1. Yisu Zhou
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Correspondence to Yisu Zhou.

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Zhou, Y., Fan, X., Wei, X. et al. Gender Gap Among High Achievers in Math and Implications for STEM Pipeline. Asia-Pacific Edu Res 26, 259–269 (2017). https://doi.org/10.1007/s40299-017-0346-1

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