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Assessing the impact of regional or statewide interventions in primary and secondary school (K-12) computer science (CS) education is difficult for a variety of reasons. Qualitative survey data provide only a limited view of impacts, but quantitative data can be notoriously difficult to acquire at scale from large numbers of classrooms, schools, or local educational authorities. In this paper, we use several publicly available data sources to glean insights into public high school CS enrollments across an entire U.S. state. Course enrollments with NCES course codes and local descriptors, school-level demographic data, and school geographic attendance boundaries can be combined to highlight where CS offerings persist and thrive, how CS enrollments change over time, and the ultimate quantitative impact of a statewide intervention. We propose a more appropriate level of data aggregation for these types of quantitative studies than has been undertaken in previous work while demonstrating the importance of a contextual aggregation process. The results of our disparate impact analysis for the first time quantify the impact of a statewide Exploring Computer Science (ECS) program rollout on economic groups across the region. Our blueprint for this analysis can serve as a template to guide and assess large-scale K-12 CS interventions wherever detailed project evaluation methods cannot scale to encompass the entire study area, especially in cases where attribute heterogeneity is a significant issue. 

This study investigated patterns in the development of computational thinking practices in the context of the Exploring Computer Science (ECS) program, a high school introductory CS course and professional development program designed to foster deep engagement through equitable inquiry around CS concepts. Past research indicates that the personal relevance of the ECS experience influences students' expectancy-value towards computer science. Expectancy-value is a construct that is predictive of career choices. We extended our research to examine whether expectancy-value influences the development of computational thinking practices. This study took place in the context of two ECS implementation projects across two states. Twenty teachers, who implemented ECS in 2016–17, participated in the research. There were 906 students who completed beginning and end of year surveys and assessments. The surveys included demographic questions, a validated expectancy-value scale, and questions about students' course experiences. The assessments were developed and validated by SRI International as a companion to the ECS course. Overall, student performance statistically increased from pretest to posttest with effect size of 0.74. There were no statistically significant differences in performance by gender or race/ethnicity. These results are consistent with earlier findings that a personally relevant course experience positively influences students' expectancy for success. These results expanded on prior research by indicating that students' expectancy-value for computer science positively influenced student learning.