Although it is well known that Black women are underrepresented in computing, less is known about their pre-college experiences. We hypothesize that inequities at the K-12 level result in Black women's underrepresentation in computing, because Black women have accumulated less social capital and are less embedded in courses and organizations related to computing prior to college. This paper reports the initial findings from the first round of a survey designed to gather the pre-college computing experiences of Black women and their peers. Black women in our sample were less likely to report participating in formal computer science (CS) education in school, slightly more likely to report participation in outside-of-school computing programs, about equally as likely to pursue computing experiences independently, and more likely to have had no pre-college computing experiences at all. We found that Black women were less likely to report that they were told they would be a good computer scientist, especially by friends, teachers, and guidance counselors, thus reflecting weaker social connections and lower levels of social capital. These findings suggest that organizational embeddedness or social ties from pre-college computing experiences may indeed be a factor in Black women's underrepresentation in computing and that access to these experiences outside of the formal classroom may be particularly important. The survey is one part of a study that will feature a second round of data collection in another state, analysis of state-level longitudinal data, and interviews with Black women.

Background and Context: Inequities in computing education have been identified based on gender, race, ethnicity, and economic status. However, extant quantitative research tends to treat demographics as siloed categories instead of accounting for the fact that many students are members of multiple minoritized groups. There is also a conspicuous lack of research that examines equity issues at multiple stages of the education pipeline. Objectives: Our research questions asked: 1) How are gender, race/ethnicity, and economic status related to the likelihood that students will enroll in computing courses in high school? 2) How are these factors related to the likelihood of enrollment in college computing courses? 3) How do these factors relate to wage earnings for students who majored in computer science in college? Method: This study analyzed education and workforce data in the United States using multilevel logistic and linear regression analyses to identify disparities among students from multiple minoritized groups at three stages of life: high school (N=135,961), college (N=199,230), and career (N=1,251). Findings: Compounding course enrollment disparities were present at the high school level for students who are Black or Hispanic/Latino/a and female, economically disadvantaged, or both. Similar results were observed at the college level. Only gender was a statistically significant predictor of wage earnings five years after college graduation, but results of this analysis may be attenuated by the fact that relatively few students who were members of multiple minoritized groups graduated with a computing degree. Implications: These findings demonstrate the importance of considering students’ intersecting identities when assessing equity in computing education. They also lay groundwork for understanding how early inequities persist across the education pipeline and how understanding disparities at one stage can inform interpretation of disparities at subsequent stages.

Across the globe, Computer Science Education has grown tremendously over the past decade to teach primary and secondary students computing ideas and tools. From integrating computational thinking in disciplines to teaching computer science as a stand alone subject, models for teacher preparation range from one and done professional learning workshops to full certificate and licensure programs. The group will focus on providing a landscape of how CS teachers are prepared academically in various countries and make evidence-based recommendations for how teachers should be educated to develop knowledge and skill to teach computer sci- ence. The working group will also discuss how to develop these knowledge systems while promoting instruction that is equitable and centers students in the classroom. In addition, the working group will focus on new directions in computing education (such as, artificial intelligence and machine learning) and their implica- tions for teacher preparation. We will bring together a group of international computer science education scholars who have been engaged in teacher preparation. In addition to what knowledge teachers need to teach CS, we will also focus on how the field is preparing teachers to think critically about AI/ML and the role of computer science in the design of technology tools to achieve goals while mitigating potential societal harms.

Most research on equity in access to and participation in computer science (CS) courses in high school has focused on general, siloed categories of students (e.g., ethnicity, gender). Using logistic regression, this study examines multiple student-, school-, and district-level predictors for access to and participation in CS. We found that the strongest predictors of whether a school offered CS were the racial/ethnic composition of the student population and the percentage of individuals in the community who hold a bachelor's degree. In examining participation in CS, we found significant differences between different intersections of race/ethnicity and gender. These results can inform interventions and policies to increase equity by signaling which factors should be taken into consideration, for whom, and in what situations.

Is quantitative data collected by CS education researchers objective? If we combine data from a set of studies that measure the same type of intervention, will that really show us the strength of that intervention? Are qualitative studies really less rigorous than quantitative because the number of participants may be as low as one?

Motivation is a powerful driver of teachers' decisions to enter the teaching profession and why they stay in the field. As computer science education (CSEd) expands and the need for well-prepared computer science (CS) teachers grows, understanding what motivates teachers to teach CS will help address challenges related to recruiting, preparing, and retaining teachers. This poster presents the development of a scale that measures teachers' motivation to teach CS. We used exploratory and confirmatory factor analyses to test and revise the scale. This resulted in a reliable, 18-item scale that measures four distinct, but related, constructs of teacher motivation to teach CS: external pressures, external benefits, student benefits, and personal enjoyment. Researchers and practitioners can utilize this scale to understand what motivates teachers to become CS educators and explore how such motivations can be leveraged to strengthen CSEd.

Quantitative research in CS education has suffered from inattention to complexities inherent in measuring educational equity. This study aims to tease apart the complexities of educational equity and advance the field by developing a disparity index for quantifying inequities and using it to investigate the importance of accounting for intersectionality and distinguishing between access to and participation in CS education. This descriptive study analyzed student demographic and course-taking data for N=1,537,073 high school students in Texas. Results showed the disparity index can be a useful tool for quantifying and assessing equity in CS education. Disparities in terms of access to and participation in CS education were compounded for students who were members of multiple underrepresented subpopulations (e.g., rural Black females). Disparities differed between measures of access and participation. Implications of this study are that accounting for the intersectionality of students' multiple social identities and distinguishing between access and participation in quantitative measures are key to understanding (and thus addressing) the complexities of educational equity.

As many U.S. states continue to work to increase and broaden participation in K-20 computing education, it is imperative to collect data and construct landscape reports to create organized efforts and strategic plans. Effective CS education interventions, and strategic plans, must be data-driven in order to ensure that all students have access to and are retained in high quality K- 20 computer science pathways (Stanton et al., 2017). The Expanding Computing Education Pathways (ECEP) Alliance, and the 23 member states, have been leaders in the development and promotion of state-level landscape reports. Several ECEP states have successfully designed, delivered, and analyzed data collection tools to landscape the current status of computer science education within their own state (e.g., Maryland, Indiana, Texas). However, there have been instances where data collected by different stakeholders have provided conflicting perspectives and viewpoints. Conflicting data, data that fails to account for intersectionality, or leaves out critical populations or context, potentially distracts time and effort from broadening participation in computing. This session will provide a platform for researchers and evaluators to discuss data relevant to BPC efforts, how to develop surveys, and how to structure and disseminate reports.

A complex web of factors can influence whether students participate in computer science (CS) during high school. In order to increase participation in CS for all students, we need to better understand who is currently participating and what factors might be hindering participation. This study utilized a large-scale, student-level dataset from the Texas Education Research Center to investigate factors that predict high school student participation in CS and advanced CS courses. Our dataset contained information on over 1.1 million Texas high school students from the 2017-2018 school year, allowing us visibility into CS course availability in schools, student course taking, and detailed demographic information. We used multilevel mixed-effects logistic regression models to explore predictive factors of student participation in CS and advanced CS courses, limiting our analysis to students whose schools offered CS. In both models, our results showed that students who took Algebra I before high school had more than double the odds of being enrolled in a CS course. This work supports and extends previous understanding of factors that are predictive of CS participation in high school, contributing to the existing literature by uncovering the importance of Algebra I before high school as a potential gatekeeper to participation in CS.

Students living in rural areas are less likely to attend schools that offer computer science (CS) courses largely because educational institutions in these remote areas lack the resources to staff teaching positions for these courses. This study investigated the impact of WeTeach\_CS, a program designed to train teachers to become certified to teach high school CS in Texas. The WeTeach\_CS collective impact model may be well suited to influence rural areas at scale because it utilizes an existing network of organizations across the state to bring high-quality professional development opportunities to teachers in remote areas. Results from a comparative interrupted time series analysis showed a significant, positive change in the rate in which the number of certified CS teachers in rural areas increased during the period of time after WeTeach\_CS began compared to the period before the program was implemented, whereas the number of teachers certified in technology applications showed no such change. Furthermore, the growth rate in the number of certified CS teachers was much higher for rural schools than urban, suggesting that collective impact models like WeTeach\_CS may be especially beneficial for rural communities.

With the goal of better understanding how to increase the computer science (CS) teacher workforce, this study examined the factors that predict eventual success in achieving teacher certification in CS. Participants (N = 500) were teachers who were certified in other subject areas and who expressed an interest in becoming certified to teach computer science in Texas. Results showed that teachers were more likely to become certified in CS if they already held a certification in another STEM field or if they had some prior knowledge in CS. The extent to which teachers participated in an online professional development course predicted certification success after controlling for prior CS knowledge and other factors whereas the number of hours spent in face-to-face CS professional development did not. These findings have important implications for policy makers and professional development providers who make investments of time and money to grow CS teacher capacity and increase student access to computer science education at the high school level.