Approches critiques des technologies en éducation et implications didactiques
Bibliographic record
Abstract
Située à la croisée des approches critiques en didactique des langues et de celles des technologies en éducation, l’étude des technologies en didactique des langues (voir, par exemple, Warschauer, 1998 ; Ollivier, 2019) dispose d’assises critiques complémentaires pour assurer son développement, si tant est qu’une masse suffisante de chercheur.ses travaillent à les structurer davantage. Dans le cadre de ce texte, nous proposons de nous focaliser sur les approches critiques des technologies en éducation, en tant qu’approches contributives de l’étude critique des technologies en didactique des langues. Pour ce faire, nous commençons par poser quelques balises des approches critiques en général. Nous les mobilisons ensuite pour le cas des technologies en éducation, en les posant comme des voies de dépassement de deux conceptions courantes de la relation "technologies – éducation" : les conceptions instrumentalistes et technodéterministes. Pour en donner une vue plus tangible, nous présentons finalement un appareillage théorique parmi d’autres (voir Collin, 2022), qui appréhende l’innovation technopédagogique au croisement des approches critiques de la technique et des études du Social Shaping of Technology (SST).
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.011 | 0.068 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".