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Record W1920966389 · doi:10.21432/t2x03h

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

2015· article· fr· W1920966389 on OpenAlexaffvenueabout
Simon Collin, Hamid Saffari, Jacob Kamta

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsImmigrationHumanitiesSociologyLibrary scienceComputer scienceGeographyArt

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.322
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2015
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Learning and TechnologySame topicImpact of Technology on AdolescentsFrench-language works237,207