Adaptation des cours présentiels en cours en ligne dans le contexte de covid-19 : quels défis, quelles solutions?
Bibliographic record
Abstract
Avec le confinement nécessité par l’apparition de la pandémie de covid-19, l’enseignement des langues a dû se tourner de façon précipitée vers des modalités d’enseignement en ligne. Considérant l’importance de développer un esprit de communauté pour faciliter la motivation, quels facteurs facilitent le succès d’une expérience d’enseignement des langues en ligne? Qu’est-ce qui peut complexifier le processus? À travers une comparaison de deux parcours – l’un en enseignement de l’espagnol langue étrangère au niveau débutant, l’autre en enseignement du français langue seconde au niveau avancé – le présent article vise à exposer quelques pistes permettant de répondre aux besoin des apprenants.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".