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This study aims to examine the current experiences of high school students in computer science (CS) courses and the factors that motivated them to continue their future enrollment. The participants were 603 high school students in grades 9 through 12 in Indiana, all of whom enrolled in at least one CS course during the 2020-2021 academic year. This research revealed that fun and meaningful CS pedagogy, knowledgeable CS teachers, and relevance to their lives and future careers enabled high school students to hold positive experiences in their CS classes. These experiences impacted students to take additional CS courses. In addition to these positive experiences, gender and early exposure to CS emerge as predictors to pursue CS courses. The findings will carry significance for policymakers and educators offering insights to enhance and broaden students’ participation and engagement in the CS course. 

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.