ÉvaTic : une base de connaissances sur l'évaluation des environnements d'apprentissage reposant sur les TIC
Bibliographic record
Abstract
Depuis quelques années, l'apprentissage en ligne a progressé de façon remarquable. Si les pratiques deviennent de plus en plus performantes, la littérature consacrée à l'évaluation demeure fragmentée. Pourtant toute la question de l'évaluation de l'efficacité de ces nouveaux modèles demeure incontournable compte tenu des enjeux stratégiques et des sommes investies. Cet article présente une base de connaissance conçue pour favoriser le partage de pratiques variées. La base de connaissance distingue de façon très nette la description d'un projet de son évaluation. Une série de critères généraux touchant les volets techniques, humains, organisationels, ergonomiques et pédagogiques permet de décrire une évaluation de façon à favoriser une diffusion efficace et compréhensible auprès d'une communauté de praticiens et de chercheurs. La base de connaissance conçue selon l'approche modèle-objet est facilement extensible et repose sur une interface de saisie et de visualisation conviale.
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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 | 0.030 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".