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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.more » « lessFree, publicly-accessible full text available February 26, 2026
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Free, publicly-accessible full text available August 12, 2025
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Background: ChatGPT became widespread in early 2023 and enabled the broader public to use powerful generative AI, creating a new means for students to complete course assessments. Purpose: In this paper, we explored the degree to which generative AI impacted the frequency and nature of cheating in a large introductory programming course. We also estimate the learning impact of students choosing to submit plagiarized work rather than their own work. Methods: We identified a collection of markers that we believe are indicative of plagiarism in this course. We compare the estimated prevalence of cheating in the semesters before and during which ChatGPT became widely available. We use linear regression to estimate the impact of students’ patterns of cheating on their final exam performance. Findings: The patterns associated with these plagiarism markers suggest that the quantity of plagiarism increased with the advent of generative AI, and we see evidence of a shift from online plagiarism hubs (e.g., Chegg, CourseHero) to ChatGPT. In addition, we observe statistically significant learning losses proportional to the amount of presumed plagiarism, but there is no statistical difference on the proportionality between semesters. Implications: Our findings suggest that unproctored exams become increasingly insecure and care needs to be taken to ensure the validity of summative assessments. More importantly, our results suggest that generative AI can be detrimental to students' learning. It seems necessary for educators to reduce the benefit of students using generative AI for counterproductive purposes.more » « lessFree, publicly-accessible full text available July 18, 2025
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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.more » « lessFree, publicly-accessible full text available May 16, 2025
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Computing Self-Efficacy in Undergraduate Students: A Multi-Institutional and Intersectional AnalysisComputing 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.more » « less
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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.more » « lessFree, publicly-accessible full text available March 7, 2025
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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.more » « lessFree, publicly-accessible full text available March 14, 2025
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Free, publicly-accessible full text available March 14, 2025
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Background: Code comprehension research has identified gaps between the strategies experts and novices use in comprehending code. In computer science (CS) education, code comprehension has recently received increased attention, and research has identified correlations between code comprehension and code writing. While there is a long history of identifying expert code-comprehension strategies, there has been less work to understand and support the incremental development of code comprehension expertise. Purpose: The goal of the paper is to identify potential code-comprehension strategies that educators could teach students. Methods: In this paper, I analyze and present examples from a novice programmer engaged in a code-comprehension task. Findings: I identify five code-comprehension strategies that overlap with previously identified, expert code-comprehension strategies. While an expert would use these strategies to produce correct inferences about code, I primarily examine a novice’s unsuccessful attempts to comprehend code using these strategies. Implications: My case study provides an existence proof that shows that these five strategies can be used by a novice. This is essential for identifying potential strategies to teach novices. My primary empirical contribution is identifying potential building blocks for developing code-comprehension expertise. My primary theoretical contribution is proposing to build code-comprehension pedagogy on specific, expert strategies that I show are usable by a novice. More broadly, I hope to encourage CS education researchers to focus on understanding the complex processes of learning that occur in between the end points of novice and expert.more » « less
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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.more » « less