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Record W2801676694 · doi:10.21432/cjlt27583

Les amis et le soutien comptent: Les compétences et besoins en littératies numériques d’élèves du secondaire en Ontario français | Friends and Support Matter: The Digital Literacies Skills and Needs of High School Students in Francophone Ontario

2018· article· fr· W2801676694 on OpenAlexaffvenueabout
Megan Cotnam-Kappel

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2018
Typearticle
Languagefr
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesSociologyPedagogyPolitical scienceLibrary scienceArtComputer science

Abstract

fetched live from OpenAlex

Cette recherche s’intéresse aux compétences en littératies numériques d’élèves de la 9e à la 11e année du sud, de l’est et du nord de l’Ontario (n=215). Le nombre d’heures que les élèves passent en ligne pour leurs travaux scolaires, le nombre de personnes qui offrent du soutien aux élèves et le nombre de personnes que les élèves aident à développer leurs compétences numériques ressortent de l’analyse comme des facteurs importants et pistes possibles pour des changements pédagogiques sur le terrain. Cette étude sert de point de départ pour de recherches futures en littératies numériques qui tiennent compte du contexte linguistique minoritaire et des voix des élèves concernés. This research focuses on the digital literacies skills and needs of students in Grades 9 to 11 in Southern, Eastern, and Northern Ontario. A quantitative analysis of data collected through online questionnaires (n=215) identifies the time students spend online on their homework, as well as both the digital support that they receive and offer as significant factors in the development of their digital literacies skills. Teachers are thereby called upon to create opportunities for collaborative learning and to renew their pedagogies to enable students to develop and demonstrate their digital literacies skills, in their languages, both within and outside of school.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.213
Teacher spread0.209 · 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
Published2018
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Learning and TechnologySame topicLiteracy, Media, and EducationFrench-language works237,207