Les stratégies volitionnelles dans l’enseignement supérieur: Se mettre au travail et s’y maintenir.
Bibliographic record
Abstract
La métacognition ?L’apprentissage autorégulé ?Quelles différences ?Quelles similitudes ?Comment les définir ?Comment les utiliser en contexte scolaire, académique, professionnel ?Voici les questions auxquelles cet ouvrage tente de répondre.Dans l’introduction, les coordinatrices dressent un historique des concepts et des travaux de recherche construits autour de cette problématique, depuis Flavell à nos jours. Les contributions de ce livre couvrent les différents niveaux de l’enseignement, de l’élémentaire au supérieur, en passant par le secondaire et l’éducation continue. Elles sont rédigées par des chercheurs enseignants francophones issus de Belgique, de France, de Suisse et du Canada.Certains chapitres, plus théoriques, intéresseront surtout les chercheurs et spécialistes du domaine, d’autres, plus proches des pratiques de classe sur le terrain, interpelleront davantage les praticiens-didacticiens qui souhaiteraient intégrer ces approches constructives et dynamiques dans leurs pratiques pédagogiques.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".