Les technologies de l’information et de la communication pour évaluer les séquences de stage : étude de cas d’un dispositif de formation professionnelle en alternance québécois
Bibliographic record
Abstract
Cet article décrit les modalités d’évaluations des stages en formation professionnelle. Il discute de l’intégration des technologies de l’information et des communications (TIC) en soutien à l’évaluation dans le cadre d’un nouveau dispositif d’alternance en cours d’implantation au Québec. À partir d’entrevues et d’observations de séances de travail, l’article identifie quatre usages prescrits du numérique lors des stages : (1) la captation d’une expérience de travail, (2) la transformation d’un objet issu d’une expérience, (3) le partage de ces expériences et objets et (4) l’enrichissement des expériences de travail. Ces usages soutiennent les évaluations formatives et sommatives (a) d’apprentissages réalisés dans le cadre des activités de travail, (b) de composantes de savoir-être au travail et (c) d’acquisition d’éléments de compétences spécifiques au nouveau programme.
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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 | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".