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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. 

Developing a repertoire of notional machines (i.e., pedagogical tools for teaching programming) is essential for new computer science (CS) educators. However, there is a lack of documentation of notional machines and related pedagogical content knowledge (i.e., insights into teaching CS content). Our experience report addresses this lack of documentation and captures insights from our professional learning community. We co-designed an approach to use physical objects to teach inheritance in Java. Unlike a research paper that would rigorously document a few student outcomes with the expectation that these would generalize, our experience report shares our observations from multiple years of teaching with the goal of providing a number of things for educators to consider when teaching inheritance in Java. Drawing from an analysis of our meeting notes, we describe our instructional sequence, our perceptions of its current strengths and weaknesses for supporting students’ learning, insights from our previous failed attempts, and eight pedagogical practices. 

Teacher isolation, where only one teacher at a school is teaching a particular subject, has been reported as one of the biggest challenges for computer science (CS) teachers in the US. However, the extent of CS teacher isolation has not been documented beyond teachers' self report. We use 14 years of middle and high school data from California to determine factors affecting the likelihood of CS being offered or a CS teacher being isolated at a school. We find that teachers in CS experience isolation at a higher rate than almost all other subjects and that larger schools are more likely to have one or more CS teachers. We extend prior work by showing that schools with a greater proportion of students underrepresented in computing are less likely to offer CS even when controlling for school size. 

Computing self-efficacy is an important factor in shaping students' motivation, performance, and persistence in computer science (CS) courses. Therefore, investigating computing self-efficacy may help to improve the persistence of students from historically underrepresented groups in computing. Previous research has shown that computing self-efficacy is positively correlated with prior computing experience, but negatively correlated with some demographic identities (e.g., identifying as a woman). However, existing research has not demonstrated these patterns on a large scale while controlling for confounding variables and institutional context. In addition, there is a need to study the experiences of students with multiple marginalized identities through the lens of intersectionality. Our goal is to investigate the relationship between students' computing self-efficacy and their prior experience in computing, demographic identities, and institutional policies. We conduct this investigation using a large, recent, and multi-institutional dataset with survey responses from 31,425 students. Our findings confirm that more computing experience positively predicts computing self-efficacy. However, identifying as Asian, Black, Native, Hispanic, non-binary, and/or a woman were statistically significantly associated with lower computing self-efficacy. The results of our work point to several future avenues for self-efficacy research in computing. 

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. 

This microteaching session is like Nifty Assignments for instruction. Instead of having the presenters just talk about their teaching, they will simulate how they would actually teach something. Covering a range of topics and grade levels, six educators will demo how they would teach a specific topic. To help identify the pedagogical practices that cut across grade bands and topics, the moderator, Colleen Lewis, will describe how their pedagogical practices connect with education research. The goal of the session is to inspire SIGCSE attendees by highlighting innovative instruction by exceptional educators. Attendees can adopt the content and/or pedagogical practices from each microteaching example.