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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. 

Paper presented as part of symposium. 

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.