We report our development of a survey measuring students' sense of belonging in computing courses. Existing definitions and surveys are helpful in identifying and measuring perceptions of belonging (e.g., feeling accepted, included, and valued) but give little insight into why students feel they do (not) belong. To address this limitation, we use a conceptual framework that breaks sense of belonging into four components: motivations, opportunities, competencies, and perceptions. Survey item development was based on interviews with undergraduate computing students that were analyzed using this conceptual framework. We revised survey items based on 1) cognitive interviews and 2) psychometric properties of pilot survey data with participants from multiple required courses typically taken by first- and second-year undergraduate computing students. The resulting survey consists of 12 items with three items per component of the conceptual framework. A four-factor confirmatory factor analysis model indicated good empirical fit with the proposed structure of the framework (N=514). Cronbach's alpha indicated good internal consistency within each component (>0.70) as well as across all 12 items (>0.90). The inter-component correlations were relatively high for all pairs of components (>0.60), aligning with previous interview findings that indicate the components may influence each other. Altogether, we have sufficient evidence of validity for the survey's use in undergraduate computing courses and discuss avenues for future work.

Sense of belonging correlates with retention rates for university-level students especially for those in their earlier years [5, 9, 10] and for those in science, technology, engineering, and math (STEM) and computing majors [2, 8]. In computing, women and students of color (i.e., Black, Latinx, and Indigenous) tend to report lower sense of belonging compared to men [6, 7]. Unfortunately, we lack consensus on how sense of belonging should be defined, and why students come to feel like they do or do not belong. Past definitions (e.g., [3, 4] help identify educational contexts and students’ feelings, they do not necessarily provide coherent insights into why students develop these feelings.

Prior work has found that women tend to report lower sense of belonging compared to men in STEM and computing contexts, which may discourage women's persistence. Collaborative learning has been shown to improve students' sense of belonging in some STEM and computing courses relative to traditional lecturing; however, these studies tend to focus on a single course or the first implementation of such pedagogical changes. Our study explores whether these trends generalize by measuring students' sense of belonging across three non-introductory computing courses that have consistently used collaborative learning activities over three semesters. We ask the following research question: Is collaborative learning generally associated with an increased sense of belonging, especially for women? We found that while there were variations across courses, students' reported sense of belonging improved in all courses. Notably, women's reported sense of belonging improved 15% whereas men's reported sense of belonging improved by 11%. Our findings complement prior studies by providing evidence of a relationship between increased sense of belonging and collaborative learning, and suggest students' sense of belonging is malleable beyond the first year. These findings challenge critiques of past studies as being isolated to single courses or conducted only immediately after an effort to change a course, suggesting pedagogical changes may hold promise in improving students' affective outcomes.

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.

In this Birds of a Feather (BoF) session, we aim to foster a safe and open environment that allows CS education (CSEd) graduate students to share about their graduate experiences and learn from each other about graduate school, conducting research, and navigating the inherently multi-disciplinary space of CSEd. We have two main goals: (1) create specific networking opportunities between CSEd graduate students attending SIGCSE TS (hereafter just "SIGCSE") in order to foster a community of support, and (2) elicit discussions on the triumphs and tribulations that come as part of being a CSEd-focused graduate student. We plan for all attendees to leave our session with connections to fellow CSEd graduate students from other institutions (through email and a dedicated Slack) and a greater awareness of the community available to them within CS education research.

Collaborative learning is an evidence-based instructional practice that has been widely used in higher education, but it is not a silver bullet and requires careful design and implementation to yield the desired benefits. Prior work has also shown that collaborative learning is especially effective for female students, who are historically underrepresented in Science, Technology, Engineering, Mathematics (STEM), and Computing fields, suggesting a promising prospect for applying collaborative learning in these disciplines. In spring and fall of 2022 at a large public university, an upper-level required computer science course using collaborative learning was offered in a hybrid format, with great flexibility in meeting time (students could meet and collaborate at the “instructor-scheduled” meeting time, or they could pick their preferred “student-scheduled” time) and team consistency (students were encouraged, but not required, to work with a fixed team throughout the semester). To understand the effects of meeting time preference and team consistency, we measured students' learning outcomes (exam performance) and experience (sense of belonging and satisfaction about team dynamics in collaborative learning), and conducted linear regression analyses. We also investigated whether the effects were different for male and female students. We found that students' meeting time preference had no significant effect on their exam performance, sense of belonging, or satisfaction, and the non-significant effects were homogeneous for both gender groups. We also found that having a consistent team had significantly positive effects on exam performance and sense of belonging, but no significant effect for satisfaction. Moreover, the effect of team consistency on exam performance was significantly stronger for female students than male students. Our findings justified the option to give students meeting time flexibility since it did not hurt their learning experience and outcome, and encouraged exploring effective approaches to forming consistent teams that make students intrinsically want to work with them. The gender difference in effects of team consistency on exam performance aligned with previous literature and served as evidence to use collaborative learning in computing and STEM classrooms.

This paper presents an observation protocol to explore group processing in cooperative learning. Use of structured roles can help facilitate cooperative learning, decrease attrition and failure rates, and improve student performance. Our observation protocol was created to capture how groups work together in small group activities and was developed by observing three different computer science courses during the Spring 2021 semester. Percent agreement using a subset of observations suggested good interrater reliability (91.29%). We also extend a previous equality metric to quantify the rates of student participation, and found that it offered good differentiation between groups where one student contributed the most and groups where students contributed equally. We present example applications of the observation protocol related to help-seeking patterns.

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.

This Research Full Paper presents an observation protocol to explore group processing in cooperative learning. Use of structured roles, such as Process Oriented Guided Inquiry Learning (POGIL) and pair programming, can help facilitate cooperative learning and help courses scale to large classroom sizes, decrease attrition and failure rates, and improve student performance. Our observation protocol was created to capture how groups work together in online, POGIL-inspired activities. A team of graduate student researchers developed the observation protocol for a variety of courses by observing three different computer science courses during the Spring 2021 semester. A total of 77 groups across all three courses were recorded, and percent agreement using a subset of the recordings suggested good interrater reliability (91.29%). We also extend a previous equality metric to quantify the rates of student participation, and found that it offered good differentiation between groups where one student contributed the most and groups where students contributed equally. We present example applications of our observation protocol related to general participation trends, the kinds of contributions students make, student-student bonding, and help-seeking patterns. Finally, we discuss future directions for use of our coding scheme as well as implications for implementing structured role-based cooperative learning online in the future.

Most prior research on effective teaching strategies, such as POGIL, has focused on synchronous, in-person settings. My work aims to study the translation of POGIL activities to a synchronous, online setting to understand how students engage in these activities and whether the best practices found in prior work extend to the online environment.