College has a lot of unwritten rules, and some of our students will matriculate knowing those rules while others don't. Research has shown that revising homework assignments to make these unwritten rules explicit can help students' academic confidence and sense of belonging. This tutorial is intended for faculty who may be interested in revising their assignments to make those unwritten rules clear to students. We will focus on Transparent Assignment Design, a framework used to make explicit the purpose, task, and criteria of assignments. Come to this tutorial for a crash course on making your assignments transparent followed by hands-on help! Please bring an assignment you are interested in revising (or we will provide samples) and your choice of editing tool (e.g., laptop, pen/paper).

Background: Instructional transparency makes a course’s learning goals, evaluation criteria, and path to success clear to students, with the goal of improving equity in higher education. Increased transparency may improve equity by bolstering students’ self-efficacy and sense of belonging in computing, both of which are correlated with persistence in the field. Purpose: We aim to understand whether there are group differences in how students perceive and benefit from instructional transparency. We are additionally interested in understanding whether perceiving instructional transparency is positively correlated with students’ self-efficacy and sense of belonging and, therefore, can contribute to the persistence of students from historically underrepresented groups in computing. Methods: To investigate these relationships, we used linear regressions to analyze survey responses from 11,046 undergraduate students from 203 institutions. Findings: We found that there are group differences in students’ perception of transparency in their CS courses: students who identify as women, first-generation college students, and/or disabled reported perceiving less instructional transparency than their peers. We also found that perceiving more transparency has a positive correlation with students’ self-efficacy and sense of belonging in computing while controlling for important confounding variables, such as prior CS experience. We further demonstrated that this relationship is different for certain groups of students: first, for Black students and first-generation college students, perceiving transparency has a larger positive impact on their self-efficacy, and second, for Hispanic students, perceiving transparency has a smaller positive impact on their sense of belonging. Contributions: Our work constitutes one of the first empirical, multi-institutional investigations of the perceptions and benefits of transparency in CS classrooms that focuses on group differences. Our work also includes a theoretical articulation of the mechanisms through which transparent teaching practices may influence students’ self-efficacy and sense of belonging in computing. Taken together, our empirical findings and theoretical argument provide important evidence for the benefits of instructional transparency in CS courses, particularly as it relates to improving equity in computing.

Cybersecurity expertise continues to be relevant as a means to confront threats and maintain vital infrastructure in our increasingly digitized world. Public and private initiatives have prioritized building a robust and qualified cybersecurity workforce, requiring student buy-in. However, cybersecurity education typically remains siloed even within computer and information technology (CIT) curriculum. This paper's goal is to support endeavors and strategies of outreach to encourage interest in cybersecurity. To this end, we conducted a survey of 126 CIT students to investigate student perceptions of cybersecurity and its major crosscutting concepts (CCs). The survey also investigates the prevalence of preconceptions of cybersecurity that may encourage or dissuade participation of people from groups underrepresented in computing. Regardless of prior learning, we found that students perceive cybersecurity as a relatively important topic in CIT. We found student perspectives on conceptual foundations of cybersecurity were significantly different (p < .05) than when simply asked about "cybersecurity," indicating many students don't have an accurate internal construct of the field. Several previously studied preconceptions of cybersecurity were reported by participants, with one misconception - that cybersecurity "requires advanced math skills" - significantly more prevalent in women than men (p < .05). Based on our findings, we recommend promoting cybersecurity among post-secondary students by incorporating elements of cybersecurity into non-cybersecurity CIT courses, informed by pedagogical strategies previously used for other topics in responsible computing.

Computing is everywhere, and it's here to stay. Computing is crucial in many disciplines and influences every discipline. It's unlikely we'll willingly return to a society unmediated by computing. How do our institutions proceed?

Broadening participation in computing (BPC) is a focus in industry and academia. Extant research focuses on what broadening participation in computing (BPC) efforts are pursued, while we propose focusing on how change happens. Our qualitative study applied John Kotter's (2012) eight-stage change framework to analyze interviews with faculty and staff engaged in BPC efforts. Illustrative examples from our interviews elucidate each of the eight stages and how they can be applied to pursue organizational change efforts that support BPC.

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.

Background: The goal of affirmative action programs (AAPs) is to address the underrepresentation of people from historically underrepresented groups (HUGs) in the workforce. People who identify as women, Black or African American, Hispanic or Latinx/a/o/*, Native American, Native Alaskan, Native Hawai’ian, and/or Pacific Islander are considered to be a part of HUGs in computing. AAPs can unintentionally signal that a person from a HUG was selected for a position based on their gender or race/ethnicity rather than their merit [2, 3, 5, 6]. We call this signal the diversity-hire narrative. In computing, prior work has found that women hear the diversity-hire narrative during their computer science (CS) internships [4], but women’s experiences surrounding the narrative have not been thoroughly explored.

In traditional scientific publishing, studies are designed, conducted and written-up, then submitted to a conference or journal for peer review. This means that any feedback from reviewers on the design of the study is too late to be acted upon. It also means that reviewers cannot detect questionable practices such as bending of the written-up study aims to match the found data, or modification of the analysis to produce a statistically significant result. Registered Reports are a new kind of scientific publication process where the study design is written-up and peer reviewed before the study is conducted. This way, changes to data collection or procedure can still be made in response to reviewer comments, and the analysis has been stated on the record. The accept/reject decision is also made in principle at this stage, which avoids the decision being based on whether results are significant and can increase researcher confidence that the work will be published. At the time of writing, Registered Reports are available at the Computer Science Education journal and are being added at ACM Transactions on Computer Education. If not publishing at these venues, preregistration is a voluntary alternative where the design is published at a trusted third-party site like the Open Science Foundation (with an embargo), but without the peer review and early accept/reject. This birds of a feather session will bring together researchers with an interest in registering their study, reviewing such studies, or who have questions or interest in this new process.

In this workshop, participants will learn to use physical Java memory models to help students develop a deep conceptual understanding of Java variables. These physical models use the metaphor of a "remote control" to introduce references. Using table-top versions of the physical models, participants will discuss and be prepared to use physical objects to help students understand: (1) how primitive variables and references variables behave in similar and different ways, (2) how reference variables that both reference an object or array can both modify its content, (3) how calling a method with an argument creates a local variable within that method, and (4) inside an object method, the variable this references the object we called the method on. Participants will receive a set of these physical models to use in their classroom.

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.

Instructors using algorithmic team formation tools must decide which criteria (e.g., skills, demographics, etc.) to use to group students into teams based on their teamwork goals, and have many possible sources from which to draw these configurations (e.g., the literature, other faculty, their students, etc.). However, tools offer considerable flexibility and selecting ineffective configurations can lead to teams that do not collaborate successfully. Due to such tools' relative novelty, there is currently little knowledge of how instructors choose which of these sources to utilize, how they relate different criteria to their goals for the planned teamwork, or how they determine if their configuration or the generated teams are successful. To close this gap, we conducted a survey (N=77) and interview (N=21) study of instructors using CATME Team-Maker and other criteria-based processes to investigate instructors' goals and decisions when using team formation tools. The results showed that instructors prioritized students learning to work with diverse teammates and performed "sanity checks" on their formation approach's output to ensure that the generated teams would support this goal, especially focusing on criteria like gender and race. However, they sometimes struggled to relate their educational goals to specific settings in the tool. In general, they also did not solicit any input from students when configuring the tool, despite acknowledging that this information might be useful. By opening the "black box" of the algorithm to students, more learner-centered approaches to forming teams could therefore be a promising way to provide more support to instructors configuring algorithmic tools while at the same time supporting student agency and learning about teamwork.

The goal of the session is to help attendees who are committed to diversity and inclusion learn to talk about different dimensions of identity (e.g., race, class, gender, sex, sexuality, etc.). The landscape of terms is always changing and we want SIGCSE attendees to feel more comfortable using current language to talk about issues related to diversity and inclusion. This special session will include eight short videos, individual reflection, and a Q&A with presenters. This is an expansion of a special session held at the NCWIT Summit in 2019; it was well received and we hope to offer it to the larger SIGCSE audience. The format also lends itself to attendees sharing the videos and discussions with their colleagues after SIGCSE, which are available at http://ncwit.org/intersectionality-videos