Les usages numériques éducatifs des élèves allophones issus de l’immigration récente: une étude exploratoire / Educational digital uses by allophone students from recent immigration: an exploratory study
Bibliographic record
Abstract
L’objectif de cet article est de dresser un portrait des usages numériques éducatifs des élèves allophones issus de l’immigration récente, notamment en lien avec leurs usages numériques non éducatifs et leur compétence numérique, en vue de soutenir leur intégration linguistique et scolaire. Deux cent trente-six élèves de classes d’accueil de l’île de Montréal ont participé à une expérimentation enregistrée sur ordinateur. Les résultats indiquent que les usages numériques éducatifs sont peu intégrés aux usages non éducatifs que développent les élèves en contexte extrascolaire, ce qui ne semble pas démarquer les élèves allophones issus de l’immigration récente des élèves occidentaux. The goal of this article is to provide an overview of the educational digital uses by allophone students from recent immigration, particularly in relation to their non-educational digital uses and their digital competence, in order to support their linguistic and academic integration. Two hundred thirty-six students from welcoming classes from the island of Montreal participated in an experiment recorded on computers. Results indicate that the educational digital uses are little integrated to non-educational uses developed by students in extracurricular settings, which does not seem to set allophone students from recent immigration apart from non-immigrant students.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".