What is a Computer Scientist?: Unpacking the Ontological Beliefs of Black and Hispanic Female Computing Students

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Abstract

Underrepresentation of Black and Hispanic women in computer science is a long-standing problem that looks bleak at every level-undergraduate and graduate. This is prompting scholars to explore reasons for these low participation rates. One framework used to understand participation and persistence in STEM fields is identity. Prior work in computer science education suggest that identity is a strong indicator of persistence in these fields. However, it is hard to understand students' perception of identity without also understanding ontological beliefs with regards to a computer scientist. In this study, we explore the nature of a computer scientist. Guided by social identity theory, we designed a study that asked students to describe their definition or ontological belief of what constitutes a computer scientist in contrast to their ability to ascribe a computer science identity to self. Leveraging qualitative methods, we interviewedn = 24 women in computer science (Black and Hispanic, undergraduate and graduate students), in order to explore the role their ontological beliefs had on their computer science identity salience. The research questions guiding this work are: (1) How do Black and Hispanic women describe or define computer scientists? (2) What impact does this definition have on Black and Hispanic women's ability to claim a computing identity? Results suggest that the wide variation in definitions has a negative impact on computer science identity salience. The findings from this work suggest that computing should consider the impacts of the current messaging of what constitutes a computer scientist.

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APA

Lopez, J., Ross, M., Garcia, A., & Uribe-Gosselin, C. (2022). What is a Computer Scientist?: Unpacking the Ontological Beliefs of Black and Hispanic Female Computing Students. In SIGCSE 2022 - Proceedings of the 53rd ACM Technical Symposium on Computer Science Education (Vol. 1, pp. 369–375). Association for Computing Machinery, Inc. https://doi.org/10.1145/3478431.3499295

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