UCLA

Despite efforts to increase representation among those enrolled, earning degrees, and working in the computing and technology industry, women across races/ethnicities and People of Color more broadly remain underrepresented in the field. Fortunately, extant literature has found that psychosocial factors like computing identity can help broaden participation for these groups by supporting their academic and career interests and persistence. However, a consistent and comprehensive measure of computing identity remains elusive.This quantitative dissertation draws on a conceptual framework developed for Women of Color in computing and uses longitudinal survey data from 1,036 undergraduate students from one of 15 research universities across the U.S. to: (a) develop a reliable measure of computing identity and assess the role of self-efficacy and sense of belonging in that measure; (b) validate the measure across time and gender and racial/ethnic groups; and (c) identify college-related factors influencing computing identity, considering variations across gender and racial/ethnic identities. This dissertation provides a nuanced account of what computing identity is and how it forms among undergraduate students, noting the unique facilitators and impediments across gender and racial/ethnic groups. In particular, findings highlight that computing identity is a multidimensional measure that incorporates a student’s self-identity as a computing person, their sense that computing is a part of their core personal identity, and their sense of belonging in the greater computing community. Hence, it is distinct from related concepts—interest, self-efficacy (competence/performance), and social recognition. While the construct of the computing identity measure is consistent across gender, racial/ethnic, and intersectional gender and racial/ethnic identities, it does not necessarily hold over time during college. Further, while the baseline computing identity and academic major variables generally fostered greater computing identity scores, separate linear regression analyses revealed distinct predictors for different student groups, implying that a one-size-fits-all approach to fostering computing identity should be avoided. For instance, interest in computing was a salient predictor of men’s computing identity but not women’s, and self-efficacy was a positive predictor for underrepresented Students of Color but not for white students. This dissertation probes these and other findings, offering implications for enhancing practice and policy and advancing theory and research.