Enseigner l’histoire à distance : un défi pour l’autonomie des enseignant.e.s québécois.es au secondaire ?
Bibliographic record
Abstract
31 enseignants ont répondu à un questionnaire en ligne, à propos de leurs pratiques d’enseignement à distance entre mars 2020 et juin 2021. L'objectif était de cerner leur degré d’intégration des TIC, leur motivation à enseigner à distance et repérer les déterminants de leurs choix pédagogiques. Les circonstances ont amené le plus grand nombre à se familiariser avec les TIC, à en explorer les fonctions de base et, dans une moindre mesure, à s’en approprier de plus complexes. Les analyses suggèrent donc que le contexte de contrainte pandémique a amené à les enseignant.e.s à faire preuve d'autonomie professionnelle. Celle-ci semble d'autant plus assurée qu.ils.elles nourrissent une représentation de l'enseignement de l'histoire qui se rapproche de "l'apprentissage-recherche"
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.238 | 0.021 |
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; both teacher heads agree on what is shown here.
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".