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Creating computer science (CS) classrooms in which all students – especially those historically excluded in CS – feel a sense of belonging is critical to equitably expanding CS education. Providing professional learning that prepares teachers to cultivate inclusive and culturally responsive environments at the scale needed to address inequities in CS education is a major challenge. This paper explores our experience scaling inclusive CS teaching through the development of a professional learning course for teachers and accompanying facilitator training. We describe key design decisions made over several iterations of the teacher-facing course and facilitator training. Our approach has aimed to balance the need for building community and safe spaces to converse about sensitive topics with the capacity to reach teachers at scale. We discuss findings from recent facilitator trainings and discuss best practices we have learned for scaling equity-based professional learning in CS. 

The push to make computer science (CS) education available to all students has been closely followed by increased efforts to collect and report better data on where CS is offered, who is teaching CS, and which students have access to, enroll in, and ultimately benefit from learning CS. These efforts can be highly influential on the evolution of CS education policy, as education leaders and policymakers often rely heavily on data to make decisions. Because of this, it is critical that CS education researchers understand how to collect, analyze, and report data in ways that reflect reality without masking disparities between subpopulations. Similarly, it is important that CS education leaders and policymakers understand how to judiciously interpret the data and translate information into action to scale CS education in ways designed to eliminate inequities. To that end, this article expands on recent research regarding the use of data to assess and inform progress in scaling and broadening participation in CS education. We describe the CAPE framework for assessing equity with respect to the capacity for, access to, participation in, and experience of CS education and explicate how it can be applied to analyze and interpret data to inform policy decisions at multiple levels of educational systems. We provide examples using large, statewide datasets containing educational and demographic information for K-12 students and schools, thereby giving leaders and policymakers a roadmap to assess and address issues of equity in their own schools, districts, or states. We compare and contrast different approaches to measuring and reporting inequities and discuss how data can influence the future of CS education through its impact on policy.