Regards et perspectives: l’évaluation au service de la qualité pédagogique des formations eLearning
Bibliographic record
Abstract
Associer l’évaluation au eLearning génère une problématique qui s’avère à la fois simple et complexe. A l’instar de Havelock (1976a), toute personne travaillant en éducation reconnaîtra d’emblée qu’il est important d’évaluer la qualité pédagogique des formations, surtout celles impliquant des transformations du système. Par ailleurs, les sommes et l’énergie investies dans l’acquisition de matériel, le développement de cours en ligne, le soutien et la formation des acteurs ainsi que dans les recherches sur l’intégration des technologies de l’information et de la communication (TIC) rendent légitimes les attentes de retombées significatives. Les projets d’envergure finançant le développement, la formation et le soutien au eLearning fourmillent à travers le monde. Par exemple, le Campus Virtuel Suisse (CVS), les programmes européens SOCRATES, Leonardo da Vinci et les Actions concertées, le fond canadien de l’Autoroute de l’information, le réseau canadien de centres d’excellence en télé-apprentissage (TL-NCE) et le programme américain PT3 «Preparing Tomorrow’s Teachers to Use Technology», ont généré des investissements considérables. (DIPF/Orig.)
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".