Deconstructing Conceptions of Rigor in Computer Science Education

Objectives : In calls for excellent and equitable Computer Science (CS) education, the word rigor often 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 abstract Principles of Rigor which included: Precision, Systematic Thought Process, Depth of Understanding, and Challenge. They also had concrete Observations of Rigor that included Time and Effort, Intrinsic Drive, Productive Failure, Struggle, Outcomes, and Gatekeeping. Participants also shared Conditions for Rigor which included Expectations, Standards, Community Support, and Resources. Implications : Our data support 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.

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Publication Details

Journal
ACM Transactions on Computing Education
Published
2025-11-11
DOI
https://doi.org/10.1145/3776542
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
0.00
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article

Deconstructing Conceptions of Rigor in Computer Science Education

Jayne Everson, Amy J. Ko, Alannah Oleson, F. Megumi Kivuva et al.
ACM Transactions on Computing Education
Teaching and Learning Programming
article

Deconstructing Conceptions of Rigor in Computer Science Education

Jayne Everson, Amy J. Ko, Alannah Oleson, F. Megumi Kivuva, Eman Sherif
article en

Abstract

Objectives : In calls for excellent and equitable Computer Science (CS) education, the word rigor often 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 abstract Principles of Rigor which included: Precision, Systematic Thought Process, Depth of Understanding, and Challenge. They also had concrete Observations of Rigor that included Time and Effort, Intrinsic Drive, Productive Failure, Struggle, Outcomes, and Gatekeeping. Participants also shared Conditions for Rigor which included Expectations, Standards, Community Support, and Resources. Implications : Our data support 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.

ACM Transactions on Computing EducationVol. 26(1)
University of Denver (US), University of Washington (US)
Openalex Percentile: Top 63%
Teaching and Learning Programming
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