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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>

<jats:p>This study examined student learning outcomes from a middle school computer science (CS) curriculum developed through a researcher-practitioner partnership (RPP) project. The curriculum is based on students creating mobile apps that serve community and social good. We collected two sets of data from 294 students in three urban districts: (1) pre- and post- survey responses on their learning experiences and attitudes toward learning CS and creating community-serving apps; (2) the apps created by those students. The analysis of student apps indicated that students were able to create basic apps that connected with their personal interests, life experiences, school community, and the larger society. Students were significantly more confident in coding and creating community-focused apps after completing the course, regardless of gender, race/ethnicity, and grade. However, their interest in solving coding problems and continuing to learn CS decreased afterward. Analyses of students’ attitudes by gender, grade, and race/ethnicity showed significant differences among students in some groups. Seventh grade students rated more positively on their attitudes than eighth graders. Students identifying with different race/ethnicity groups indicated significantly different attitudes, especially students identifying as Southeast Asian, Black/African American, and Hispanic/Latino. Self-identified male students also reported stronger interest and more positive attitudes overall than self-identified female students. Students also reported positive experiences in learning how to create real apps serving their community, while there were disparities in their experiences with coding in general and some of the instructional tools used in the class.</jats:p>

<jats:p>Culturally responsive computing (CRC), that centers sociopolitical issues and transformational uses of technology, has been described as valuable for increasing engagement with computing, especially for historically underrepresented minoritized students. But what do high school students think? Through a sociocultural lens prioritizing student voices recorded in 56 interviews over a period of 2 years (1-3 years after students’ first experience with CS education through Exploring Computer Science or Advanced Placement Computer Science Principles in high school), this study centers the perspectives of 39 primarily low-income, Latine and Black youth from urban California and rural Mississippi public schools to understand what they perceive as the role of technology in our world and what they subsequently desire of their computing education. While none have studied CRC before, the majority responded with CRC ideas about the kind of pedagogy they believe would make for a more meaningful computing learning experience: They see computing as a form of power that impacts both good and bad in the world, and want computing educators to prepare them to take on these issues of equity, ethics, social responsibility, and underrepresentation in the field. The students’ perspectives offer important pedagogical insight into how to support deeper engagement with computing in current Computer Science for All initiatives, while also preparing youth for the rapidly evolving and increasingly complex computing landscape that impacts all of our lives.</jats:p>

<jats:p>This special issue builds on and expands computing’s engagement with Black feminist epistemologies like Intersectionality and Black Feminist Thought, exploring the intersectional experiences of Black girls and women in computing, technology, and computing education and workforce. The set of articles examines, explores, and uncovers structural and systemic barriers in computing, CS education, and technology; the roles of social supports and social capital in ensuring Black women thrive; quantitative, qualitative, and mixed methods approaches that center Black girls and women instead of making them comparative groups to white or other people of color of all genders; and issues around equity and inclusivity in computing, CS education, and technology more broadly. Taken together, this collection serves as a model for centering one community often marginalized in computing, technology, and computing education: Black girls and women.</jats:p>

<jats:p>This paper presents a systematic literature review of professional development programs in computational thinking (CT). CT has emerged as an essential set of skills that everyone should develop to participate in a global society. However, there were no pre-service or in-service teacher programs to integrate CT into the K–12 classrooms until very recently. Thus, it is important to identify how educators and researchers address the challenges to prepare the next generation of students and what gaps persist in the current literature. We review existing work in this field from two perspectives: First, we analyze the learning outcomes, assessment methods, pedagogical approaches, and pedagogical tools used in the professional development programs in CT. Second, we examine how these programs assess the teachers’ knowledge and skills as outcomes. We used the technological pedagogical and content knowledge (TPACK) framework to characterize existing literature and identify possible gaps in the preparation of pre-service and in-service teachers in CT. Our results suggest that (1) existing evidence is limited to developed countries; (2) many studies are only focusing on teachers understanding the concepts but do not explore how the participants evaluate or create learning activities; (3) no studies look into classroom observations as part of the program, which limits our understanding to how these programs work; and (4) most programs use block-based programming languages as the tool to develop student CT. While block-based programming languages are used for introductory training programs, students are often expected to transfer their learning to more professional programming languages.</jats:p>

<jats:p>Learning to respond to a computer program that is not working as intended is often characterized as finding a singular bug causing a singular problem. This framing underemphasizes the wide range of ways that students and teachers could notice discrepancies from their intention, propose causes of those discrepancies, and implement interventions. Weaving together a synthesis of the existing research literature with new multimodal interaction analyses of teacher–student conversations during coding, we propose a framework for debugging that foregrounds this open-endedness. We use the framework to structure an analysis of three naturalistic debugging situations (with US 5th–10th graders) that range from solo debugging to collaborative discourse. We argue that a broken computer program is a polysemous object through which teachers and students actively and publicly notice, reason about, and negotiate different debugging pathways. We document students and teachers improvisationally altering a debugging pathway, justifying a particular pathway, and outwardly discussing competing pathways. This paper provides a framework for structuring debugging pedagogy to be less about scaffolding a student toward a specific pathway to a fix and more about exploring multiple possible pathways and judging the (learning) value of various routes.</jats:p>

<jats:p>Integrated computing curricula combine learning objectives in computing with those in another discipline, like literacy, math, or science, to give all students experience with computing, typically before they must decide whether to take standalone CS courses. One goal of integrated computing curricula is to provide an accessible path to an introductory computing course by introducing computing concepts and practices in required courses. This study analyzed integrated computing curricula to determine which CS practices and concepts are taught, how extensively the curricula are taught, and, by extension, how they might prepare students for later computing courses. The authors conducted a content analysis to examine primary and lower secondary (i.e., K-8) curricula that are taught in non-CS classrooms, have explicit CS learning objectives (i.e., CS+X), and that took 5+ hours to complete. Lesson plans, descriptions, and resources were scored based on frameworks developed from the K-12 CS Framework, including programming concepts, non-programming CS concepts, and CS practices. The results found that curricula most extensively taught introductory concepts and practices, such as sequences, and rarely taught more advanced content, such as conditionals. Students who engage with most of these curricula would have no experience working with fundamental concepts, like variables, operators, data collection or storage, or abstraction in the context of a program. While this focus might be appropriate for integrated curricula, it has implications for the prior knowledge that students should be expected to have when starting standalone computing courses.</jats:p>

<jats:p>Machine learning (ML) models are fundamentally shaped by data, and building inclusive ML systems requires significant considerations around how to design representative datasets. Yet, few novice-oriented ML modeling tools are designed to foster hands-on learning of dataset design practices, including how to design for data diversity and inspect for data quality.</jats:p> <jats:p>To this end, we outline a set of four data design practices (DDPs) for designing inclusive ML models and share how we designed a tablet-based application called Co-ML to foster learning of DDPs through a collaborative ML model building experience. With Co-ML, beginners can build image classifiers through a distributed experience where data is synchronized across multiple devices, enabling multiple users to iteratively refine ML datasets in discussion and coordination with their peers.</jats:p> <jats:p>We deployed Co-ML in a 2-week-long educational AIML Summer Camp, where youth ages 13–18 worked in groups to build custom ML-powered mobile applications. Our analysis reveals how multi-user model building with Co-ML, in the context of student-driven projects created during the summer camp, supported development of DDPs including incorporating data diversity, evaluating model performance, and inspecting for data quality. Additionally, we found that students’ attempts to improve model performance often prioritized learnability over class balance. Through this work, we highlight how the combination of collaboration, model testing interfaces, and student-driven projects can empower learners to actively engage in exploring the role of data in ML systems.</jats:p>