Perspectives actuelles sur la formation à l’ère du numérique
Bibliographic record
Abstract
Ce numéro compte trois articles de recherche, un article de praticiens et deux articles de discussions et débats offrant une exploration de divers sujets au cœur de la technologie éducative. D’une part, l’hybridation de l’enseignement, une approche complexe intégrant différentes modalités pour offrir des expériences d’apprentissage optimisées. D’autre part, les pratiques d’interaction en contexte de formation à distance, soulignant l’importance de maintenir des connexions significatives malgré les barrières physiques. Ce numéro explore également la télésurveillance, un outil essentiel pour préserver l’intégrité intellectuelle et lutter contre la fraude dans les examens en ligne. De plus, une réflexion approfondie sur le leadership et la stratégie numérique en contexte scolaire émerge de ces pages, mettant en lumière l’importance d’une vision éclairée pour tirer pleinement parti des technologies éducatives. Enfin, l’édition varia 2023 aborde le lien entre la technologie éducative et la formation en santé, explorant la formation thérapeutique des patients ainsi que la littératie médicale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".