Pratiques d’enseignement et conceptions de l’enseignement et de l’apprentissage d’enseignants du primaire à divers niveaux du processus d’implantation des TIC
Bibliographic record
Abstract
Résumé : Cet article décrit les pratiques et les conceptions de huit enseignants du primaire qui se situent à divers stades du processus d’implantation des TIC. Les résultats, issus d’entretiens et d’observations, montrent que les enseignants qualifiés d’expérimentateurs, qui se situent au niveau inférieur d’utilisation des TIC, ont des conceptions et des pratiques plutôt béhavioristes. Les enseignants collaborateurs ou adaptateurs, qui se situent à des niveaux supérieurs du processus d’implantation des TIC, ont des pratiques plutôt variées et affichent plus de conceptions constructivistes que les enseignants expérimentateurs. La discussion analyse les liens entre les conceptions affichées par les enseignants et les usages des TIC. Teaching practices and elementary school teacher’s concepts of teaching and learning at different levels of integration of ICTs Abstract: This article describes both practices and concepts of eight elementary school teachers who are at different stages of implementation of ICT in their practice. The results, taken from interviews and observation, indicate that teachers, identified as “experimenters” and who tend to use ICT less, adopt concepts and practices that lean towards behaviourism. Teachers identified as “collaborators” or “adaptors” and who are at higher levels of integration of ICTs tend to have practices that are more varied and show evidence of internal representations leaning more towards constructivism. The discussion further analyzes the relationship between the concepts that are evoked by the teachers and their uses of ICTs.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".