Teaching Sustainable Computing Through Repair: Case Studies on Curriculum Design
Bibliographic record
Abstract
Addressing the global e-waste crisis and reducing the carbon produced during operation, and equally, manufacturing, of consumer and datacenter electronics necessitates not only incremental technical improvements, but more broadly, a paradigm shift towards slower and more sustainable computing practices. To contend with these issues, both computer engineers and the general public need a better understanding of how their personal use of computers and their work relate to their social and environmental contexts. We argue that teaching electronic and computer repair is a great place to begin these conversations, by giving students the opportunity to develop practical hands-on skills through experiential, situative learning, and then linking these concrete experiences to more abstract discussions of sustainable computing. We have developed and run a year long course for university students, and workshops aimed at K12 students, both of which center around teaching sustainable computing and hands-on skills through electronic and computer repair. We designed and evaluated this course material based on surveys and interviews with students, repair experts, and community members. In this poster, we present our curricula and course materials and explain the pedagogical theory and research that underpin our approach, as well as how we adapted our work for different student groups and course formats. We enumerate challenges that we encountered while implementing these lessons, and provide recommendations for other educators interested in teaching repair courses or workshops.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".