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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. 

Collective impact is an approach for solving complex social problems at scale. The challenge of broadening participation in computing (BPC) is one such problem. The complexity of BPC is compounded by the decentralized nature of public education, where decisions are made primarily at the state level and subject to interpretation at the district level. As such, diversifying computer science (CS) pathways across the nation requires a systemic approach such as collective impact to engage all of the stakeholders who influence CS education and whose decisions can either facilitate or hinder BPC efforts. This experience report discusses how the collective impact framework has been used to advance the work of the Expanding Computing Education Pathways (ECEP) Alliance, an NSF funded BPC Alliance focused on states and state policy as the unit of change. We discuss how the five essential features of collective impact (common agenda, shared measurement, mutually reinforcing activities, continuous communication, and backbone support) coalesce to facilitate ECEP's theory of change. The report highlights specific policy changes that ECEP states have addressed to promote BPC, the flipped accountability that results from a non-hierarchical leadership model, and the challenges of measuring systemic changes as an intermediary to BPC. 

This experience report details the lessons learned while launching a Networked Improvement Community (NIC) with 23 teachers in Texas as part of the NSF-funded Accelerating Women’s Success and Mastery in Computer Science (AWSM in CS) project. Conceived to address the persistent gender inequities in computer science (CS) education, the NIC was designed to bring together researchers and practitioners to collaboratively develop and implement solutions with the goal of increasing female participation in CS courses. This experience report explores the lessons learned, such as the importance of building a sense of community, trust, and collaboration, before jumping into problem solving as a NIC. Additionally, the report addresses considerations for sustaining the NIC virtually given the logistical constraints placed on teacher collaboration during the school year.