Enhancing Sustainable Engineering Education with Codesign and Script Concordance: A work in progress
Bibliographic record
Abstract
This paper introduces a new approach to engineering education that integrates script concordance and codesign principles in a sustainable production course. The engineer's role constantly evolves amid the uncertainty of dynamic challenges, necessitating the development of reasoning skills during their education. Script concordance assesses clinical reasoning skills under uncertainty, while codesign is a participatory method involving stakeholders in the design process. A collaborative team of students (bachelor and master), lecturers, professors, and pedagogical experts to design script concordance scenarios in a sustainable development context. We conducted several workshops to co-create and validate the scenarios, which covered topics such as multidisciplinary, multi-criteria analysis, multidimensional reasoning, and ontology. The desired results are for students to be able to describe their reasoning plausibly and to justify it in problems where no precise answers enable causal relationships. This will bridge the gap between theory and practice that employers may observe in the field of engineering.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".