« Le sac d’école électronique »: un outil technologique pouvant faciliter la mise en place de l’alignement curriculaire / The “digital school bag”: A technological tool to facilitate the implementation of curriculum alignment
Bibliographic record
Abstract
Cet article discute des réformes du système éducatif québécois qui s’inscrivent dans le courant des réformes entreprises un peu partout dans le monde avec des résultats variables. Plusieurs facteurs sont évoqués parmi lesquels le manque de formation et de soutien des enseignants, malgré les actions du ministère de l’Éducation, du Loisir et du Sport du Québec qui a confié la recherche de moyens de soutien à différents organismes dont la Maison des technologies de formation et d'apprentissage Roland-Giguère (MATI Montréal) qui a conçu un logiciel «le sac d’école» peu connu et utilisé. Le but de cet article est de présenter cet outil technologique et comment il peut permettre d’atteindre l’alignement curriculaire, élément clé de toute réussite de réforme éducative. This paper discusses the education-system reforms in Quebec, which follow the same trends as reforms undertaken elsewhere in the world with various results. Several factors are invoked, among which is the lack of training and support for teachers despite the efforts of Quebec’s Ministère de l’Éducation, du Loisir et du Sport, which assigned various organizations to looks into support methods. These organizations included the Maison des technologies de formation et d’apprentissage Roland-Giguère (MATI Montréal), which designed a “school bag” software that is little used. The paper introduces the technological tool and shows how it can help in reaching curriculum alignment, a key element for the success of educational reforms.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".