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Pathways to the professoriate for women in computer science are narrow and fraught with barriers. These obstacles are further exacerbated at the intersections of race and gender. Black women (who make up 6.4% of the U.S. population) comprise only 1.1% of computer science undergraduate degrees and < 1% of computer science PhDs. Despite these paltry numbers, one computer science PhD program may have found the combination of factors necessary to widen the pathway by engaging in strategic recruitment, developing communities of practice, and providing strong mentorship for women of color in computer science. Guided primarily by intersectionality theory, social identity theory, and landscapes of practice, this single case study explored the experiences of Black women in pursuit of their doctorate in computer science at a predominantly white institution to answer the research questions: (1) How do Black women graduate students in computer science describe their computer science identity? (2) How do landscapes of practice influence computer science identity formation or salience of Black women in a computer science graduate program? Thematic analysis of this case revealed three common themes within their experiences: moments of impact, boundary spanning, and community residence. These themes, all of which revolve around ideas of community and support, are critical to understanding a key discovery of this study: why a sense of belonging, rather than identity salience (as much research suggests), was the best indicator of the women’s persistence. 

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 under- standing 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 com- puter science identity to self. Leveraging qualitative methods, we interviewed n= 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. 

Currently, the computer science community is experiencing a rise in interest in computer science education research (CSER). However, current structures and belief systems within the discipline have largely relegated computer science education researchers to the margins. Computer science education researchers are mainly 'lone-wolf' scholars in their departments that participate in CSER in addition to their more widely 'accepted' computer science research. As such, there has been a resistance to offer doctoral programs in CSER. Florida International University has not only invested inCSER by hiring a CSER tenure-track faculty member, they have also established a School of Computer Science Education and Engineering Education. Despite this investment, one obstacle remains the low visibility and understanding of computer science education research among undergraduate students. This makes establishing are search group of Ph.D. students challenging. In order to combat this obstacle, a four-year program was developed as a dedicated pipeline to the computer science education Ph.D. through a series of research experiences for undergraduates (REU). Summer 2020 consisted of the first cohort of this four year commitment. Given that 2020 was wrought with a series of unprecedented events, this REU was designed and executed virtually. This paper presents the plan, setting, execution, and subsequent evaluation of this virtual REU experience. Student-feedback was overwhelmingly positive; however, as with any endeavor there were many lessons learned. 

Although computing occupations have some of the greatest projected growth rates, there remains a deficit of graduates in these fields. The struggle to engage enough students to meet demands is particularly pronounced for groups already underrepresented in computing, specifically, individuals that self-identify as a woman, or as Black, Hispanic/Latinx, or Native American. Prior studies have begun to examine issues surrounding engagement and retention, but more understanding is needed to close the gap, and to broaden participation. In this research, we provide quantitative evidence from the Multiple-Institution Database for Investigating Engineering Longitudinal Development—a longitudinal, multi-institutional database to describe participation trends of marginalized groups in computer science. Using descriptive statistics, we present the enrollment and graduation rates for those situated at the intersection of race/ethnicity and gender between 1987 and 2018. In this work, we observed periods of significant flux for Black men and women, and White women in particular, and consistently low participation of Hispanic/Latinx and Native American men and women, and Asian women. To provide framing for the evident peaks and valleys in participation, we applied historical context analysis to describe the political, economic, and social factors and events that may have impacted each group. These results put a spotlight on populations largely overlooked in statistical work and have the potential to inform educators, administrators, and researchers about how enrollments and graduation rates have changed over time in computing fields. In addition, they offer insight into potential causes for the vicissitudes, to encourage more equal access for all students going forward. 

Computer science (CS) has been identified as one of the fastest-growing professions, with demand for CS professionals far outpacing the supply of CS graduates. The necessity for a trained CS workforce has compelled industry and academia to evaluate strategies for broadening participation in CS. The current literature in CS education emphasizes the importance of social relationships and supports for individuals from underrepresented groups. Unfortunately, this literature has largely been limited to either the exploration of issues of women or that of underrepresented racial/ethnic groups. These limited views generalize characteristics of specific underrepresented groups without considering intersections between these groups. This quantitative study ( n = 3,206) addressed that shortcoming by leveraging inferential statistical methods to examine (i) the similarities and differences between the social CS-related experiences of Black women, Black men, and non-Black women in the United States; (ii) the relationship between these experiences and CS career choices; and (iii) the activities during which significant social experiences might occur. The results indicate that Black women's social experiences are often different from the experiences of both Black men and non-Black women. In particular, both Black men and non-Black women had more CS friends than Black women, whereas having these friends was more significant for the CS career choice for Black women. Introductions to CS in school, before college, were negatively related to career choice for all groups, whereas home support was positive for both Black women and men. This work suggests that considering intersectionality is important to understanding the needs of different individuals, as well as the importance of social supports for persistence in CS.