Le contexte de la pandémie mondiale comme possible source d’innovation : apprentissage expansif et résolution de contradictions
Bibliographic record
Abstract
The closure of schools caused by the COVID-19 pandemic has caused a great deal of concern in educational circles. Indeed, students no longer had access to traditional education in schools. Teachers had to adapt quickly to the new emergency measures and teach remotely, in virtual mode. In addition to having to learn a variety of digital tools, they were under pressure in terms of the quality of their teaching and the supervision of students. The unusual and uncertain climate caused by the pandemic has therefore led several actors in the school community to reflect on their practice to improve the educational experience of students for the future return to class. This mandatory change in pedagogical strategy represents a challenge likely to create tensions among teachers who have decided to use new, more student-centered pedagogical approaches. This article, anchored in the third generation of activity theory, is based on a systematic review of the literature, and analyzes how the challenges generated by virtual teaching have been able to promote the expansive learning of teachers. This review of the literature suggests possible contradictions representing the four levels established by Engeström (2001) and proposes steps likely to lead to a sustainable transformation of the activity. In conclusion, it argues that the resolution of the contradictions experienced by teachers will promote both the transformation of their teaching practices as well as their adaptation to the new virtual reality.
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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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.030 | 0.022 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.005 |
| 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".