Retour sur l’apprentissage de formateurs d’entraîneurs
Bibliographic record
Abstract
Le but de cette etude est d’analyser les apprentissages de formateurs du Programme national de certification des entraineurs du Canada ayant recemment adopte une approche par competences et centree sur l’apprenant. Utilisant la theorie de l’apprentissage humain (Jarvis, 2006), l’etude adopte une perspective existentielle dans laquelle l’apprentissage est un processus individuel dans un contexte social. D’apres Jarvis, un individu a la possibilite d’apprendre lorsqu’il a une experience comprenant un contenu dissonant, c’est-a-dire ne concordant pas avec, par exemple, ses connaissances developpees lors d’experiences anterieures. En apprenant, il retrouve l’harmonie en reduisant l’ecart entre la situation presente et ses experiences anterieures. Les donnees recueillies a partir d’entretiens semi-structures et d’observations non participantes aupres de cinq formateurs demontrent que ces derniers ont ete en dissonance lors de leurs seances de formation et de la facilitation d’ateliers. L’interpretation des resultats a permis de faire deux recommandations aux responsables de la formation des formateurs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".