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<jats:p> <jats:bold>Research Problem.</jats:bold> <jats:bold>Computer science (CS)</jats:bold> 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. </jats:p> <jats:p> <jats:bold>Research Question.</jats:bold> Our research question for this study was: <jats:italic>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?</jats:italic> </jats:p> <jats:p> <jats:bold>Methodology.</jats:bold> 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 <jats:bold>CAPE</jats:bold> theoretical dissaggregation framework to measure schools’ <jats:bold>Capacity</jats:bold> to offer CS, student <jats:bold>Access</jats:bold> to CS education, student <jats:bold>Participation</jats:bold> in CS, and <jats:bold>Experiences</jats:bold> of students taking CS. </jats:p> <jats:p> <jats:bold>Data Collection Procedure.</jats:bold> We developed an instrument to collect qualitative data from open-ended questions, then collected data from CS high school educators ( <jats:italic>n</jats:italic> = 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 ( <jats:italic>n</jats:italic> = 185), analyzed the data, and considered how these findings shape research conducted over the last year. </jats:p> <jats:p> <jats:bold>Findings.</jats:bold> Overall, practitioner perspectives revealed that capacity for CS Funding, Policy &amp; 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. </jats:p> <jats:p>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.</jats:p>

<jats:p>A rich body of empirically grounded results and a solid theory base have often been viewed as signs of a mature discipline. Many disciplines have frequently debated what they should accept as legitimate kinds of theories, the proper roles of theory, and appropriate reference disciplines. Computing education research (CER) in particular has seen a growing number of calls for the development of domain-specific theories for CER, an adaptation of theories from other fields, and engagement with theory-based experimental and predictive research in CER. Many of those calls share the same concerns and aims, yet they use very different vocabulary and lack a consensus over an essential concept: theory.</jats:p> <jats:p>This article presents sticking points and trouble spots in CER’s theory debates and presents a number of suggestions and ways forward. Firstly, by slightly shifting towards a model-based view of science, CER can avoid centuries of conceptual baggage related to the concept of theory. Secondly, insofar as fields like design, engineering, and social science are considered to be legitimate parts of CER, the role of theory in many CER studies needs to be judged by the criteria of the philosophy of engineering, technology, and social science, not the philosophy of (natural) science. Thirdly, instead of force-fitting elements of ill-suited research paradigms from other disciplines, the philosophy of CER should focus on building a consensus on CER’s own paradigm and describing the field’s relationship with theory in CER’s own terms.</jats:p>

<jats:p>Several authors of articles in the special issue came together for an asynchronous discussion of the articles, surfacing several tensions and opportunities for future work. This summary of the discussion offers a glimpse into these insights.</jats:p>