L’évaluation des SAMI (système d’apprentissage multimédia interactif) : de la théorie à la pratique
Bibliographic record
Abstract
Cet article traite de la mise au point d'une stratégie de collecte de données visant à regrouper et à analyser les facteurs sous-jacents au développement d'une interactivité significative dans des situations d'apprentissage médiatisé. Les auteurs ont conçu un modèle adapté d'analyse axée principalement sur une approche qualitative; ils ont utilisé à cette fin des méthodes intégrant la technique de l'entrevue, l'observation directe et l'analyse de protocoles. Celles-ci ont permis de cerner les facteurs perceptifs, transactionnels, cognitifs, pédagogiques et évaluatifs qui peuvent diriger la prise de décision des concepteurs de systèmes d'apprentissage multimédia interactifs. Des mises à l'essai de ce modèle à l'aide de concepteurs issus des milieux de formation scolaire ont indiqué la pertinence et le raffinement des analyses possibles.
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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 | 0.047 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".