Patterns d’interactions écrites asynchrones entre des classes branchées en réseau
Bibliographic record
Abstract
Résumé : L’étude s’inscrit dans le contexte de l’initiative québécoise de l’École éloignée en réseau (l’ÉÉR) visant à enrichir la qualité de l’environnement d’apprentissage des petites écoles rurales. L’étude s’intéresse de façon spécifique aux interactions asynchrones qui surviennent entre des classes distantes géographiquement par le biais d’un forum électronique de coélaboration de connaissances (le Knowledge Forum®). Nous nous sommes penchés sur le cas spécifique d’une commission scolaire en documentant la façon dont les interactions qui ont eu lieu entre les élèves de ses écoles impliquées dans l’initiative se sont orchestrées sur une période de deux années scolaires. Pour ce faire, nous avons principalement utilisé des analyses quantitatives descriptives. Les résultats démontrent la viabilité du modèle de mise en réseau pour faire interagir des élèves de classes différentes de sorte qu’ils puissent bénéficier d’un plus large bassin d’idées. Ils révèlent aussi que la collaboration en réseau s’organise autour de différents niveaux de complexité et que cette dernière varie selon les temps de l’année scolaire. Abstract:This study was conducted in the context of the Remote Networked Schools (RNS) initiative that aims at enriching Quebec’s rural schools learning environment. Specifically, the researchers studied how geographically distant classrooms interacted on Knowledge Forum®, a web-based collaborative space. The particular case of a school board was studied by documenting how school learners interacted asynchronously over a two year period. Descriptive quantitative analyses were applied. Results show the viability of the RNS model for student to student interaction in a way as to increase the idea pool. Results also reveal that network collaboration self-organizes at different levels of complexity, ones that vary according to the school-year schedule.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".