Usages des technologies en éducation : analyse des enjeux socioculturels
Bibliographic record
Abstract
Cet article propose une analyse des enjeux socioculturels qui accompagnent les usages des technologies en éducation. Nous commençons par relever que, malgré leur pertinence éducative et socioprofessionnelle, les technologies en éducation doivent faire face à plusieurs défis, notamment parce qu’elles ne tiennent pas compte des usages technologiques des apprenants en dehors des institutions éducatives. Sur la base de ce constat, deux objectifs sont poursuivis : le premier consiste à dresser un portrait des usages technologiques des apprenants en dehors des institutions éducatives. Le second se propose d’en déduire des implications pour orienter les usages des technologies en contexte éducatif. Pour ce faire, nous procèderons dans un premier temps à une analyse des usages technologiques des jeunes. Nous en déduirons ensuite des implications pour orienter les usages des technologies en éducation, notamment en proposant un modèle « élargi » des technologies en éducation.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".