Sustainable Engineering Design in Education: A Pilot Study of Teaching Right‐to‐Repair Principles through Project‐Based Learning
Bibliographic record
Abstract
Over 60 million tons of E-waste is expected to be generated in 2023, with associated significant impacts on health and the environment. To reduce the number of products sent to landfills, "Right to Repair" (RtR) movements are gaining momentum in many countries, including the UK, USA, and EU member states. While Universities are seen as important stakeholders to drive forward sustainable design practices, there is currently little work looking at training undergraduate design engineers in the principles of designing household products in support of RtR. In particular, the project-based learning (PBL) pedagogy shows promise in engaging and training students with the skills and knowledge required to successfully design products for RtR. In this paper, a pilot-study of teaching engineers is presented to design products compatible with RtR principles, alongside many technical skills, in a first-year PBL course. The key outputs of this paper are the design of the module, which can be used to help inform first-year engineering education, the high engagement of students, with 100% of respondents agreeing that they intend to try to implement sustainable design practices in future, and some of the innovative features that students implement in their projects.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".