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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. 

Technology should be accessible and inclusive, so designers should learn to consider the needs of different users. Toward this end, we created the theoretically-grounded CIDER assumption elicitation technique, an educational analytical design evaluation method to teach inclusive design skills. CIDER ( Critique , Imagine , Design , Expand , Repeat ) helps designers recognize and respond to bias using the critical lens of assumptions about users . Through an eleven-week mixed-method case study in an interaction design course with 40 undergraduate students and follow-up interviews, we found that activities based on the CIDER technique may have helped students identify increasingly many types of design bias over time and reflect on their unconscious biases about users. The activities also had lasting impacts, encouraging some students to adopt more inclusive approaches in subsequent design work. We discuss the implications of these findings, namely that educational techniques like CIDER can help designers learn to create equitable technology designs. 

Motivation: Although CS Education researchers and practitioners have found ways to improve CS classroom inclusivity, few researchers have considered inclusivity of online CS education. We are interested in two such improvements in online CS education—besides being inclusive to each other, online CS students also need to be able to create inclusive technology. Objectives: We have begun developing a new approach that we term “embedded inclusive design” to address both of these goals. The essence of the approach is to integrate elements of inclusive design education into mainstream CS coursework. This paper presents three curricular interventions we have developed in this approach and empirically investigates their efficacy in online CS post-baccalaureate education. Our research questions were: How do these three curricular interventions affect (RQ1) the climate among online CS students and (RQ2) how online CS students honor the diversity of their users in the tech they create? Method: To answer these research questions, we implemented the curricular interventions in four asynchronous online CS classes across two CS courses within Oregon State University’s Ecampus and conducted an action research study to investigate the impacts. Results: Online CS students who experienced these interventions reported feeling more included in the major than they had before, reported positive impacts on their team dynamics, increased their interest in accommodating diverse users, and created more inclusive technology designs than they had before. Discussion: These results provide encouraging evidence that embedding elements of inclusive design into mainstream CS coursework, via the interventions presented here, can increase both online CS students’ inclusivity toward one another and the inclusivity of the technology these future CS practitioners create. 

Although the need for gender-inclusivity in software is gaining attention among SE researchers and SE practitioners, and at least one method (GenderMag) has been published to help, little has been reported on how to make such methods work in real-world settings. Real-world teams are ever-mindful of the practicalities of adding new methods on top of their existing processes. For example, how can they keep the time costs viable? How can they maximize impacts of using it? What about controversies that can arise in talking about gender? To find out how software teams "in the trenches" handle these and similar questions, we collected the GenderMag-based processes of 10 real-world software teams---more than 50 people---for periods ranging from 5 months to 3.5 years. We present these teams' insights and experiences in the form of 9 practices, 2 potential pitfalls, and 2 open issues, so as to provide their insights to other real-world software teams trying to engineer gender-inclusivity into their software products. 

Inclusive design is important in today's software industry, but there is little research about how to teach it. In collaboration with 9 teacher-researchers across 8 U.S. universities and more than 400 computer and information science students, we embarked upon an Action Research investigation to gather insights into the pedagogical content knowledge (PCK) that teachers need to teach a particular inclusive design method called GenderMag. Analysis of the teachers' observations and experiences, the materials they used, direct observations of students' behaviors, and multiple data on the students' own reflections on their learning revealed 11 components of inclusive design PCK. These include strategies for anticipating and addressing resistance to the topic of inclusion, strategies for modeling and scaffolding perspective taking, and strategies for tailoring instruction to students' prior beliefs and biases.