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Gender Differences in Motivational and Curricular Pathways Towards Postsecondary Computing Majors

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Abstract

Gender disparities persist in postsecondary computing fields, despite improvements in postsecondary equity overall and STEM fields as an aggregate. The entrenchment of this issue requires a comprehensive, longitudinal lens. Building on expectancy-value theory, the present study examines the relationships among students’ gender-ability stereotypes, attainment values, course-taking, and major choices. Using data from the High School Longitudinal Study of 2009 (HSLS: 2009), we applied weighted t-tests and multiple-group structural equation modeling to investigate how motivational beliefs (i.e., gender-ability stereotypes, attainment values) and course-taking patterns in math and science may predict major choice in computing. Overall, we find gender differences in identity-based mathematics and science motivational beliefs have long-term effects. Gender-ability stereotypes in math and science shape attainment values in each domain, whereby stereotypes suppress girls’ attainment values and enhance boys? attainment values (p < 0.001), in turn shaping course-taking and major decisions. Math- and sciencerelated motivational and curricular factors affect “other” STEM more than computing major outcomes. Specifically, computer science course-taking is completed more by boys (d = 0.21), but girls’ chances of declaring computing majors are especially enhanced by completing these courses in high school. Advanced science course-taking and science attainment value positively predict boys’ but not girls’ likelihood of declaring computing majors. We discuss the implications of these findings for research, policy, and practice.

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Availability of Data and Materials

Public use access to these data is available through the National Center for Education Statistics website, as is the application for restricted use data, such as we used for these analyses: https://nces.ed.gov/surveys/hsls09/hsls09_data.asp. Our restricted-use license number is 12100041. Statistical code generated to analyze supporting the findings of this study are available from the corresponding author upon request.

Notes

  1. We recognize distinctions between gender and sex, whereby the latter typically refers to binary and biological notions of male/female in distinction to gender which is developed through socialization and realized via gendered behavior, performance, and identity. This manuscript is constrained by the binary nature of data procured by the federal government from U.S. high schools., Wherever possible, we refer to boys/girls and men/women and use the term “gender” because of our focus on gender stereotypes, gender-role identities, and other constraints from socialization into the gender system. See also Perez-Felkner et al. (2023) and Ridgeway and Smith-Lovin (1999).

  2. While we generally avoid using male/female in this manuscript (see also footnote 1), we do here and in the corresponding results discussion use “males” and “females” when it refers to the language used in the gender stereotype items described here.

  3. Perez-Felkner et al. (2019) investigated the relationship between gender, institutional type, and STEM clusters but not distinct conceptual framing, data, and methodology.

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Acknowledgements

Earlier versions of this manuscript benefitted from formative feedback from Dr. Yanyun Yang and from our internal research colleague Ciera Fluker.

Funding

This work was supported by generous funding from the National Science Foundation, under grant #1920670. This work represents the work of the authors and does not represent the views of the National Science Foundation.

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Authors and Affiliations

  1. Department of Educational Psychology and Learning Systems, Florida State University, Tallahassee, United States

    Jinjushang Chen

  2. Department of Educational Leadership and Policy Studies, Florida State University, Tallahassee, United States

    Lara Perez-Felkner, Shouping Hu & Yang Li

  3. Center for Postsecondary Success, Florida State University, Tallahassee, United States

    Jinjushang Chen, Lara Perez-Felkner, Chantra Nhien, Shouping Hu & Yang Li

  4. Knowli Data Science, Tallahassee, United States

    Kristen Erichsen

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  1. Jinjushang Chen
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  2. Lara Perez-Felkner
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  3. Chantra Nhien
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  4. Shouping Hu
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  5. Kristen Erichsen
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  6. Yang Li
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Correspondence to Lara Perez-Felkner.

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Chen, J., Perez-Felkner, L., Nhien, C. et al. Gender Differences in Motivational and Curricular Pathways Towards Postsecondary Computing Majors. Res High Educ (2023). https://doi.org/10.1007/s11162-023-09751-w

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