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Record W2139640893 · doi:10.21432/t2g03g

Outils numériques nomades : les effets sur l’attention des étudiants

2015· article· fr· W2139640893 on OpenAlexvenueno aff
Nicolas Guichon, Salifou Koné

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesSociologyArt

Abstract

fetched live from OpenAlex

La recherche sur le numérique en éducation est parvenue à une étape où peut être dressé un portrait plus nuancé des usages des outils numériques en contexte d’apprentissage. S’inscrivant dans une perspective sociocritique (Selwyn, 2007), cet article vise à apprécier les effets éventuellement négatifs résultant de l’utilisation des outils numériques nomades pendant le face-à-face didactique. Une enquête par questionnaire a été conduite auprès d’un échantillon d’étudiants internationaux (n=227) poursuivant leurs études dans un Centre de Langues en France. Cette recherche a permis d’examiner leurs habitudes de connexion en mettant au jour la fréquence et la motivation d’utilisation d’outils numériques pendant et hors le face-à-face avec leurs enseignants. Elle a également donné l’occasion de saisir l’impact ressenti de l’utilisation des outils numériques en classe par les participants sur leur attention. Les résultats de cette recherche permettent, in fine, d’examiner de quelle façon le contrat didactique est questionné par l’usage des outils numériques nomades.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.143
GPT teacher head0.390
Teacher spread0.246 · 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 designObservational
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 routes1
Has abstractyes

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