Utiliser la pensée design comme stratégie d’enseignement pour résoudre un problème environnemental dans le cadre des STIM
Bibliographic record
Abstract
Dans un monde en evolution constante, les competences fondamentales developpees par les STIM (sciences, technologies, ingenierie et mathematiqes) seront essentielles pour tout le monde, surtout devant une planete davantage affectee par des problemes environnementaux de plus en plus serieux. C'est connu que la transformation societale vers un avenir plus responsable sur le plan de l'environnement passe par l'education. Or, quels sont les meilleurs moyens d'enseigner des competences STIM? La pensee design represente une facon creative et collaborative d'apprendre durant laquelle l'intuition compte, les solutions sont nombreuses, l'experimentation arrive rapidement, les echecs sont valorises dans un processus d'apprentissage et, surtout, les besoins des usagers sont pris en compte. Dans cette etude, menee dans le cadre d'un cours universitaire en ingenierie, nous experimentons l'application d'une approche pedagogique axee sur la pensee design, dans le but de demontrer empiriquement qu'une telle approche peut contribuer a developper des competences STIM et mener a la solution de problemes environnementaux.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".