Materiality Matters in Computing Education: A Duoethnography of Two Digital Logic Educators
Bibliographic record
Abstract
Computer science needs to be sustainable, and CS educators have an important role to play in changing the discipline. Recent efforts have emerged to teach CS in ways that apply computing to mitigate climate change, but this alone is insufficient: we must also change what it means to do computing. We use duoethnography to interrogate our practices as CS educators to support the goal of integrating sustainability into CS. Despite being invested in these goals, we each realized that we had been nevertheless reinforcing the cultural norms that underpin the environmental and social damage caused by computing. We found five themes in the qualitative analysis of our reflections: (1) A lack of materiality in CS classes makes it difficult for computer scientists to scrutinize the environmental costs of hardware, (2) The discourse on “greenness” in computing neglects the role of embodied emissions, (3) The lack of context in CS education teaches students to perceive the status quo as “natural", (4) Those who have bought into the dominant ideology of CS can be resistant to innovating CS education, and (5) Materiality helps with teaching computing. We illustrate how changing CS to become more sustainable requires deeper thought than “add sustainability and stir” to the curriculum, and insights toward addressing the root ideology of CS education.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.029 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".