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ObjectivesIn calls for excellent and equitable Computer Science (CS) education, the wordrigoroften appears, but it often goes undefined. The goal of this work is to understand how CS teachers, instructors, and students conceive of rigor. Research Questions:1) What do CS instructors think rigor is? and 2) What do students think rigor is? Methods:Using the principles of phenomenological research, we conducted a semi-structured interview study with 10 post-secondary CS students, 10 secondary CS teachers, and 9 post-secondary CS instructors, to understand their conceptions of rigor. Results:Analysis showed that no participants had the same understanding of rigor. We found that participants had abstractPrinciples of Rigorwhich included: Precision, Systematic Thought Process, Depth of Understanding, and Challenge. They also had concreteObservations of Rigorthat included Time and Effort, Intrinsic Drive, Productive Failure, Struggle, Outcomes, and Gatekeeping. Participants also sharedConditions for Rigorwhich included Expectations, Standards, Community Support, and Resources. Implications:Our data supports prior work that educators are using different definitions of rigor. This implies that each educator holds different expectations for students, without necessarily communicating these expectations to their students. In the best case, this might confuse students; in the worst case, it reinforces hegemonic norms which can lead to gatekeeping which prevents students from fully participating in the CS field. Based on these insights, we argue that to commit to the idea of quality CS learning, the community must discard the use of this concept of rigor to justify student learning and re-imagine alternate benchmarks. 

Background and Context: With the growing movement to adopt critical framings of computing, scholars have worked to reframe computing education from the narrow development of programming skills to skills in identifying and resisting oppressive structures in computing. However, we have little guidance on how these framings may manifest in classroom practice. Objectives: To better understand the processes and practice of critical pedagogy in a computing classrooms, we taught a critically conscious computing elective within a summer academic program at a northwest United States university targeted at secondary students (ages 14–18) from low-income backgrounds and would be the first in their families to pursue a post-secondary education (i.e., first-generation). We investigated: (1) our participants’ initial perceptions of and attitudes toward the benefits and perils of computing, and (2) potential tensions that might emerge when secondary students negotiate the integration of critical pedagogy in a computing classroom. Methods: We qualitatively coded participant work from a critically conscious computing course within a summer academic program in the United States focused on students from low-income backgrounds or would be the first in their family to pursue a post-secondary education. Findings: Our participants’ initial attitudes toward technology were mostly positive, but exhibited an awareness of its negative impacts on their lives and society. Throughout the course, while participants demonstrated a rich social consciousness around technology, they faced challenges in addressing hegemonic values embedded in their programs, designs, and other classwork. Implications: Our findings revealed tensions between our participants’ computing attitudes, knowledge, self-efficacy, and social consciousness, suggesting pathways for scaffolding the critical examination of technology in secondary education. This study provides insights into the pedagogical content knowledge necessary for critical computing education. 

Introduction: Computer science (CS) lacks representation from people who identify as one or more of the following identities: woman, Black, Indigenous, Hispanic, Latina/Latino/Latinx, or disabled. We refer to these groups as historically underrepresented groups (HUGs). Informal learning, like CS summer camps and hackathons, can increase interest in K-12 students but still struggles to broaden participation. Objectives: In this study, we examine one source of struggle for informal learning programs: recruiting practices. Methods: Toward the goal of understanding this struggle, we interviewed 14 informal K-12 CS learning programs across a diverse region in the Northwestern United States to understand what recruiting practices are being used. We used a cultural competency lens to examine the variation within recruiting practices and how some practices could lead to broader participation in computing. Results: We identified 18 different recruiting practices used by informal CS learning program organizers. Some programs had similar practices, but subtle differences in implementation that led them to fall at different points on the cultural competence continuum. More culturally competent implementations generally involve reflection on the needs of specific populations that programs were trying to recruit, on why previous recruiting implementations did not work, and on feedback from stakeholders to change their implementations. This is the first article to investigate how the implementation of the recruiting practice determines its cultural competency. Conclusion: Results from this study illuminate some of the problems informal CS programs face in broadening participation in computing and provide insights on how program organizers’ can overcome them. Our work highlights how students or parents access resources, the challenges program organizers encounter, and whether current recruiting practices effectively engage students from HUGs. 

For computing to serve humanity, computing spaces must be safe for all individuals. While prior work has surfaced how hegemonic racial and gendered expectations manifest in computing, it has only indirectly attended to expectations surrounding neurodivergence. As computing stereotypes largely align with stereotypes of some neurodivergent individuals, we investigated whether computing legitimized neurodivergent traits over neuronormative ones. We conducted semi-structured interviews with 21 students, faculty, and industry professionals, sampling both neurodivergent-identifying and non-neurodivergent-identifying participants. We found that computing legitimized hyper-focus, deep “special” interests, and high organization, and that fitting these expectations was frequently required for persistence. Some neurodivergent-identifying participants felt that computing provided refuge from societal neuronormative expectations, though one’s sense of refuge depended on sufficiently fitting computing’s neurodivergent expectations. We offer reflections on inclusion and belonging efforts within computing, as well as directions for future work that attends to individuals’ neurodivergent identities.