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Record W2791962439 · doi:10.18162/fp.2018.394

Usages et perceptions des enseignants lors de l’utilisation de la tablette en contexte scolaire

2018· article· fr· W2791962439 on OpenAlexaffvenue
Aurélien Fiévez, Thierry Karsenti

Bibliographic record

VenueFormation et profession · 2018
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Formation et profession 26(1), 2018 sum L'utilisation des tablettes en contexte scolaire est de plus en plus visible dans les salles de classe en Amrique du Nord et en Europe. L'intgration de cette nouvelle technologie induit des modifications dans les activits d' enseignement et d'apprentissage. La tablette apporte de multiples perspectives et de nouveaux usages pdagogiques. Elle modifie l'administration et la gestion quotidienne de l' enseignement de manire prgnante. De par ses caractristiques intrinsques, elle suppose galement un apprentissage travers le temps et l' espace. Devant ces multiples enjeux, nous avons analys les usages de la tablette en salle de classe auprs de 200 enseignants de 45 tablissements scolaires au Qubec. Les rsultats mettent en vidence que l'utilisation de la tablette induit des usages pdagogiques novateurs qui facilitent l' enseignement et favorisent l'apprentissage des lves. Cependant, nous constatons que la tablette n' est pas utilise son plein potentiel et que certains usages devraient tre envisags selon une perspective plus efficiente et adapte.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

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.

Opus teacher head0.046
GPT teacher head0.387
Teacher spread0.341 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2018
Admission routes2
Has abstractyes

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