Savoir-enseigner et approche constructiviste des apprentissages en formation initiale des maîtres : Les paramètres du développement professionnel dans les productions étudiantes des futurs enseignants Franco-Ontariens
Bibliographic record
Abstract
A partir d’une experience que nous avons menee dans un cours universitaire de planification de l’enseignement amenage selon le paradigme constructiviste de l’apprentissage, nous montrons, a travers cet article, comment de futurs maitres assurent a leur niveau un developpement autonome du savoir enseigner. L’accent est mis sur la construction des connaissances dans la matiere etudiee en prenant appui sur les divers processus d’elaboration de sens et de developpement professionnel dans lesquels s’engagent les futurs maitres. Les resultats montrent que meme s’il a pour cadre premier le contexte isole de la formation universitaire, le cheminement des futurs maitres se revele comme un processus complexe de participation ou se joue l’entree de l’etudiant dans les meandres des pratiques de toute une communaute. Le constructivisme est le cadre de reference a partir duquel sont analyses les apprentissages effectues par les futurs enseignants a l’interieur du cours. L’experience etudiee a ete menee aupres de la cohorte 2003-2004 des etudiants de l’Ecole des sciences de l’education a l’Universite Laurentienne.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".