Critical Pedagogy in Practice in the Computing Classroom
Bibliographic record
Abstract
To enact social justice in the computer science classroom, we need to go beyond adding token ethics modules to Computer Science (CS) curricula and to rethink the power structures in our pedagogical practices. Critical pedagogy (CP) is a long-standing pedagogical tradition that aims to re-envision power structures in the classroom, but has been relatively underutilized in computing education. To go beyond theoretical ideas of what CP should be in CS, we interviewed 13 computing educators who identified as being influenced by critical pedagogy. We asked participants about their teaching practices, and how they apply CP ideals in their classrooms. To illustrate themes from our interviews and to give a rich description of what a CP-influenced classroom looks like, we present three vignettes highlighting a contrast of approaches to critical CS education: raising students' critical consciousness to see structures of oppression, helping students learn technology that supports their activism, and changing what it means to do computer science by integrating social and political forces. By providing tangible, concrete examples we hope to provide educators with inspiration for their own practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.074 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".