Search for: All records

Students from historically underrepresented communities in computer science (CS) report being told that their successes are due to special treatment based on their gender and/or racial identity. We refer to this microaggression as the discounting-success microaggression. We analyzed survey responses from 4,327 CS majors across 221 institutions in the U.S. We found that students who identify as women, Black, and/or Asian were more likely than men and white students, respectively, to report the discounting-success microaggression. This discounting-success microaggression significantly and negatively predicts students’ self-efficacy, sense of belonging, and plans to persist in CS. Our results elucidate the negative influence of the discounting-success microaggression on CS student outcomes. Efforts are needed to improve the culture and interactions in CS to eliminate the prevalence of this harmful microaggression. 

Background : Affirmative action programs (AAPs) aim to increase the representation of people from historically underrepresented groups (HUGs) in the workforce, but can unintentionally signal that a person from a HUG was selected for their identity rather than their merit. We call this signal the diversity-hire narrative. Prior work has found that women hear the diversity-hire narrative during their computer science (CS) internships, but women and non-binary students' experiences surrounding the narrative are important to understand and have not been thoroughly explored. Objectives: We seek to understand the (1) sources and (2) impacts of this narrative, as well as (3) how students respond to it. Methods: We conducted and qualitatively analyzed 23 semi-structured interviews with undergraduate CS students in the gender minority (i.e., students who identify as women or non-binary). Results : Participants reported hearing the diversity-hire narrative from family and peers. They reported feeling self-doubt and a double standard where their success was not attributed to their intelligence, but their peers' success was. Participants responded to the diversity-hire narrative by (1) ignoring it, (2) attempting to prove themselves, (3) stating that their peers are jealous, (4) explaining that AAPs address inequity, and (5) explaining that everyone is held to a high standard. Implications: These results expand our understanding of the experiences that likely impact undergraduate CS students in the gender minority. This is important for broadening participation in computing because results indicate that students in the gender minority often encounter the diversity-hire narrative, which deprives them of recognition by invalidating their hard work. 

Artificial intelligence (AI) and cybersecurity are in-demand skills, but little is known about what factors influence computer science (CS) undergraduate students' decisions on whether to specialize in AI or cybersecurity and how these factors may differ between populations. In this study, we interviewed undergraduate CS majors about their perceptions of AI and cybersecurity. Qualitative analyses of these interviews show that students have narrow beliefs about what kind of work AI and cybersecurity entail, the kinds of people who work in these fields, and the potential societal impact AI and cybersecurity may have. Specifically, students tended to believe that all work in AI requires math and training models, while cybersecurity consists of low-level programming; that innately smart people work in both fields; that working in AI comes with ethical concerns; and that cybersecurity skills are important in contemporary society. Some of these perceptions reinforce existing stereotypes about computing and may disproportionately affect the participation of students from groups historically underrepresented in computing. Our key contribution is identifying beliefs that students expressed about AI and cybersecurity that may affect their interest in pursuing the two fields and may, therefore, inform efforts to expand students' views of AI and cybersecurity. Expanding student perceptions of AI and cybersecurity may help correct misconceptions and challenge narrow definitions, which in turn can encourage participation in these fields from all students.