Sustainability Literacy and Repair: A Case Study of Effective Sustainability Pedagogy in Electrical and Computer Engineering
Bibliographic record
Abstract
Addressing the rapid increase in global e-waste production, and the embodied carbon of consumer electronics requires a shift towards a more sustainable computer engineering paradigm centered around “slow consumption” practices like repair. We argue that effective sustainability education is a critical part of effecting this change. Through surveys and interviews with students, repair experts, and community members, we investigate existing attitudes and levels of sustainability literacy among engineering students, and outline opportunities for meaningful teaching and learning. We develop, deliver, and evaluate a university-level course centered on electronic repair, drawing on evidence-based pedagogical strategies for meaningfully building sustainable development competencies. The aim of the course was to build both practical repair skills and critical sustainability competencies, and to bring students into conversations and longer term collaboration with the broader community. Our findings indicate that the course successfully resulted in shifts in student attitudes and that students reported a commitment to ongoing community engagement and personal action. The findings underscore the need for meaningful integration of environmental literacy into engineering curricula, through implementation of evidence-based pedagogical strategies, in order to train future engineers with an understanding of the relationship between their work and the environment.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".