Bibliographic record
Abstract
Le texte explore l’évaluation des apprentissages au Québec, guidée par la Politique d’évaluation des apprentissages. Deux fonctions sont distinguées : l’aide à l’apprentissage, qui fournit des rétroactions pour ajuster l’enseignement et la reconnaissance des compétences, souvent par des examens formels. Ces examens, imposés trois fois par an, peuvent désavantager les élèves ayant des difficultés, nuisant à l’équité. Pour une évaluation plus inclusive, l’article propose des méthodes alternatives, notamment la rétroaction par les pairs et l’usage de jeux pédagogiques, comme le Noggle, pour évaluer la compétence de résolution de situations-problèmes en mathématique. Ces approches, intégrées aux activités d’apprentissage, engagent activement les élèves et prennent en compte leur diversité, tout en renforçant leur estime de soi et leur sentiment de compétence en mathématique. Elles permettent ainsi une évaluation plus juste et adaptée à tous les élèves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.064 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".