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 âgreennessâ in computing neglects the role of embodied emissions, (3) The lack of context in CS education teaches students to perceive the status quo as ânatural", (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 âadd sustainability and stirâ to the curriculum, and insights toward addressing the root ideology of CS education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".