Les environnements d’apprentissages riches en technologie et leur impact sur les compétences du 21e siècle
Bibliographic record
Abstract
L’ecole a un role a jouer dans la formation des futurs citoyennes et citoyens et, dans un monde de plus en plus numerique, elle doit offrir un milieu d’apprentissage permettant le developpement des competences recherchees par le milieu du travail d’aujourd’hui, notamment des competences du 21 e siecle. Nous nous demandons comment l’ecole peut faire pour mieux developper des competences du 21 e siecle chez les eleves, a l’aide des TIC. Nous nous interessons particulierement a l’environnement d’apprentissage numerique et au potentiel d’un tel milieu educatif a mieux preparer la jeunesse pour l’economie du 21 e siecle, une economie davantage numerique. En definissant d’abord un environnement d’apprentissage qui est considere riche en technologies, nous explorons ensuite quels genres d’environnement d’apprentissage riche en technologies existent (p. ex. : les labos de fabrication numerique) et sont exploites autour du monde. Nous precisons ensuite quelles competences non techniques sont developpees dans de tels environnements.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 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".