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Black women remain severely underrepresented in computing despite ongoing efforts to diversify the field. Given that Black women exist at the intersection of both racial and gendered identities, tailored approaches are necessary to address the unique barriers Black women face in computing. However, it is difficult to quantitatively evaluate the efficacy of interventions designed to retain Black women in computing, since samples of computing students typically contain too few Black women for robust statistical analysis. Using about a decade of student survey responses from an National Science Foundation–funded Broadening Participation in Computing alliance, we use regression analyses to quantitatively examine the connection between different types of interventions and Black women’s intentions to persist in computing and how this compares to other students (specifically, Black men, white women, and white men). This comparison allows us to quantitatively explore how Black women’s needs are both distinct from—and similar to—other students. We find that career awareness and faculty mentorship are the two interventions that have a statistically significant, positive correlation with Black women’s computing persistence intentions. No evidence was found that increasing confidence or developing skills/knowledge was correlated with Black women’s computing persistence intentions, which we posit is because Black women must be highly committed and confident to pursue computing in college. Last, our results suggest that many efforts to increase the number of women in computing are focused on meeting the needs of white women. While further analyses are needed to fully understand the impact of complex intersectional identities in computing, this large-scale quantitative analysis contributes to our understanding of the nuances of Black women’s needs in computing. 

BRIDGES is a software framework for creating engaging assignments for required courses such as data structures and algorithms. It provides students with a simplified API that populates their own data structure implementations with live and real-world data, and provides the ability for students to easily visualize the data structures they create as part of routine classroom exercises. The objective is to use the infrastructure to promote a better understanding of the data structure and its underlying algorithms. This report describes the BRIDGES infrastructure and provides evaluation data collected over the first five years of the project. In the first 2 years, as we were developing the BRIDGES projects, our focus was on gathering data to assess whether the addition of the BRIDGES exercises had an effect on student retention of core concepts in data structures; and throughout the 5-year duration of the project, student interest and faculty feedback were collected online and anonymously. A mixed method design was used to evaluate the project impact. A quasiexperimental design compared student cohorts who were enrolled in comparable course sections that used BRIDGES with those that did not. Qualitative and quantitative measures were developed and used together with course grades and grade point averages. Interest and relevance in BRIDGES programming assignments was assessed with additional survey data from students and instructors. Results showed that students involved in BRIDGES projects demonstrated larger gains in knowledge of data structures compared to students enrolled in comparable course sections, as well as long-term benefits in their performance in four follow-on required courses. Survey responses indicated that some investment of time was needed to use BRIDGES, but the extra efforts were associated with several notable outcomes. Students and instructors had positive perceptions of the value of engaging in BRIDGES projects. BRIDGES can become a tool to get students more engaged in critical foundational courses, demonstrating relevance and context to today’s computational challenges. 

This full paper presents the Collaborative Active Learning and Inclusiveness (CALI) inventory, and an analytical model using the CALI inventory, demographic data, mindset surveys, and knowledge mastery assessment, to explore relationships between classroom climate and student experiences. The CALI inventory enables the investigation of the impact of the student experience in an active learning classroom by distinguishing the factors that characterize the structure, social learning, and inclusive practices. The Structure Index includes components related to course setup, organization, assessment, grading, and communications. The Sociality Index includes components related to opportunities for students to interact with each other. The Inclusiveness Index includes components related to how the instructor communicates a sense of belonging to the students through a growth mindset and inclusive policies and practices. A CS Mindset Instrument was developed based on research that measured students' self-efficacy by evaluating the extent of variation in their self-perceived ability to accomplish a task, sense of belonging in computing, and professional identity development. Demographic data is collected that allows for an analysis using an intersectional lens to acknowledge the complexity of social and cultural contexts. The knowledge and mastery assessments capture changes in competency through pre-post mastery quizzes. The combination of CALI with other instruments, including those that characterize student mindset, identity, and levels of mastery, enables investigation of how various practices of inclusive and collaborative active learning have differential effects on students with different identities in computer science. 

Freshmen and sophomore level courses in computer science are critical to long-term student development and success. At the same time, these courses, such as data structures and algorithms are usually challenging and require significant motivation to keep students engaged. In this work, we present through our BRIDGES system a set of location based assignments that can serve to reinforce core concepts and algorithms by placing them in more meaningful settings and applications, and demonstrate the relevance of computing in the early stages of a student's career. We performed a small pilot study using a subset of these assignments in a special topics course on algorithms, and conducted student surveys after each assignment. The surveys were unanimously positive, and the students enjoyed coding the algorithms as well as the datasets and visualizations associated with the assignments.