L’enseignement par projets en sciences et technologies : de quoi parle-t-on et comment justifie-t-on le recours à cette approche?1
Bibliographic record
Abstract
L’article traite de l’enseignement par projets en sciences et technologies aux niveaux primaire et secondaire. Plus particulièrement, il vise à comprendre la manière dont cet enseignement est défini et justifié par les chercheurs qui se sont intéressés à la question. L’analyse de 76 articles dans 16 revues scientifiques montre qu’un nombre limité d’attributs et d’éléments de justification revient dans le discours de ces auteurs. La poursuite des apprentissages disciplinaires y occupe une place importante. L’analyse permet aussi de dégager au sein de ces éléments ceux qui relèvent des visées, des apprentissages disciplinaires poursuivis, et ceux qui relèvent des moyens et des conditions nécessaires à la réussite de cet enseignement. L’article souligne aussi l’importance de mener des études sur les pratiques de classe dans lesquelles les enseignants recourent à cet enseignement.
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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 | 0.065 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".