Quels outils de communication pour améliorer les formations en ligne
Bibliographic record
Abstract
La formation en ligne pour les enseignants et les directions d’ecole est un element essentiel du developpement de la societe Canadienne. Notre etude porte sur les apprenants des cours en ligne d’un programme de maitrise. Nous les avons interroges par le biais d’un questionnaire en ligne sur leurs profils, les usages des outils de communication et sur leurs perceptions de la formation et du programme. Les resultats de cette recherche quantitative montrent qu’un facteur est la contrainte des apprenants : les apprenants inscrits a temps partiel ont moins d’opportunite de suivre des activites synchrones. Aussi, les resultats tendent a montrer que l’important pour developper des formations Web2.0 n’est pas tant d’integrer ou de separer les outils, mais plutot d’associer des outils prives et publics pour chaque modalite. Abstract Online learning for teachers and school administrators is a crucial part of the development of Canadian society. This article studies online learners participating in a Master’s program. We used an online questionnaire to interview them about their profiles, their use of communication tools and about their perception of the training program. The results of this quantitative study show that the learners’ constraints were a factor: the learners enrolled on a part-time basis had fewer opportunities to participate in synchronous activities. Furthermore, the results tended to show that it was more important, in the development of Web 2.0 training programs, to associate private and public tools with each learning mode, than it was to integrate or separate the tools.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".