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
Bibliographic record
Abstract
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.
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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 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".