Caractérisation des transactions didactiques : Deux études de cas en Découverte Du Monde Vivant au cycle II de l'école élémentaire
Bibliographic record
Abstract
Didactic communication is a spécification of human communication. The Joint Action Theory in Didactic (JATD, Sensevy, Mercier &al. 2005, 2007 et 2008), considers the teacher's and student's discourse production as the didactics transactions demonstration. By this way, knowledge is seen as the transaction's stake. These transactions allow at the same time the interlocutors' mutual recognition and their participation to a joint game. This Game consists in building and changing the world, thanks to more or less specific learning games. This works' major intention is to understand how and why the teacher produces his discourse in the class. This study's context concerns two first graduate classes committed in a Discovery Living World lesson. This fundamental research works out an investigation framework able to describe how knowledge organizes didactics transactions. This framework joint three components : didactic enonciative strategies, teacher's practical epistemology and learning games. The pattern's development and implementation are based on the case study of two teachers: one beginner and one experienced. This analysis pattern uses at the same time didactic and linguistic approaches. It allows to grasp some of the determinations which influence the joint action of teacher and students and to turn a first characterization of the didactic transactions.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".