Le développement professionnel autonome chez les enseignants dans le contexte de la pandémie
Bibliographic record
Abstract
Through this research we explored whether the cognitive, social and technical mediations developed between certain education actors, in the particular context of a disruption of practices due to the COVID-19 pandemic, have led to professional development among teachers working in elementary and secondary schools in Quebec. Forced to teach remotely or in hybrid mode by a long period of confinement, teachers had to keep in touch with students and ensure the continuity of their learning. This context has led teachers to turn to various mediation resources, such as through peers, social media or educational advisers. Operationalized by changes in practice in the context of an unprecedented health crisis, the professional development of the 29 participating teachers was studied using the Q methodology. The classifications of the statements developed for this purpose highlight three teacher profiles: those whose professional development was mainly based on interactions with known colleagues, teachers who relied more on their reflections and pedagogical advisers and those who were inspired by professional exchanges on social media to develop their practice. Related to teaching in specific contexts, our results could be used by pedagogical advisers to better support the development of professional skills allowing the adaptation of teaching practices to unusual situations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".