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With more recognition being given to the diverse and changing demographics in education, there is a need to understand how well computer science education is meeting the needs of all learners as it starts to infiltrate K-12 schools. The CAPE framework is a newer model for assessing the equitable delivery of computer science education and can be used to understand a school’s capacity to offer equitable computer science (CS) education, equitable student access to CS education, equitable student participation in CS, and equitable experiences of students taking CS. Since the CAPE framework is a new way to research CS education through an equity-lens, there are few, if any, frameworks that can be leveraged to explore research questions in a complex, multi-school intervention. To address this gap, we used a design-based research approach to create and determine the feasibility of a new model, Theory of Impacts, informed by the CAPE framework (the ToI-CAPE model), for evaluating a multi-school intervention. In this article, we provide a detailed explanation of creating and using the ToI-CAPE model for a specific intervention and the feasibility of using ToI-CAPE across factors based in experiences and how to use this model in other research and evaluation projects. Overall, the use of the ToI-CAPE model can be used to shed light on the critical subcomponents and agents at work in the intervention and the actions necessary across these components and agents to support intended outcomes. 

Research Problem. Computer science (CS) education researchers conducting studies that target high school students have likely seen their studies impacted by COVID-19. Interpreting research findings impacted by COVID-19 presents unique challenges that will require a deeper understanding as to how the pandemic has affected underserved and underrepresented students studying or unable to study computing. Research Question. Our research question for this study was: In what ways has the high school computer science educational ecosystem for students been impacted by COVID-19, particularly when comparing schools based on relative socioeconomic status of a majority of students? Methodology. We used an exploratory sequential mixed methods study to understand the types of impacts high school CS educators have seen in their practice over the past year using the CAPE theoretical dissaggregation framework to measure schools’ Capacity to offer CS, student Access to CS education, student Participation in CS, and Experiences of students taking CS. Data Collection Procedure. We developed an instrument to collect qualitative data from open-ended questions, then collected data from CS high school educators (n = 21) and coded them across CAPE. We used the codes to create a quantitative instrument. We collected data from a wider set of CS high school educators ( n = 185), analyzed the data, and considered how these findings shape research conducted over the last year. Findings. Overall, practitioner perspectives revealed that capacity for CS Funding, Policy & Curriculum in both types of schools grew during the pandemic, while the capacity to offer physical and human resources decreased. While access to extracurricular activities decreased, there was still a significant increase in the number of CS courses offered. Fewer girls took CS courses and attendance decreased. Student learning and engagement in CS courses were significantly impacted, while other noncognitive factors like interest in CS and relevance of technology saw increases. Practitioner perspectives also indicated that schools serving students from lower-income families had 1) a greater decrease in the number of students who received information about CS/CTE pathways; 2) a greater decrease in the number of girls enrolled in CS classes; 3) a greater decrease in the number of students receiving college credit for dual-credit CS courses; 4) a greater decrease in student attendance; and 5) a greater decrease in the number of students interested in taking additional CS courses. On the flip-side, schools serving students from higher income families had significantly higher increases in the number of students interested in taking additional CS courses. 

With the growth of computing education research in the last decade, we have found a call for a strengthening of empiricism within the computing education research community. Computer science education researchers are being asked to focus not only the innovation that the research creates or the question it answers, but also on validating the claims we made about the work. In this session, we will explore the relationship between evaluation and computing education research and why it is so vital to the success of the many computing education initiatives underway. It will also help computing faculty engaged in computer science education research understand why it is essential to integrate evaluation and validation from the very first conceptual stages of their intervention programs. 

With the growth of computing education research in the last decade, we have found a call for a strengthening of empiricism within the computing education research community. Computer science education researchers are being asked to focus not only the innovation that the research creates or the question it answers, but also on validating the claims we made about the work. In this session, we will explore the relationship between evaluation and computing education research and why it is so vital to the success of the many computing education initiatives underway. It will also help computing faculty engaged in computer science education research understand why it is essential to integrate evaluation and validation from the very first conceptual stages of their intervention programs. 

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.