L'utilisation de tablettes numériques dans des classes de troisième secondaire : retombées, difficultés, exigences et besoins de formation émergents
Bibliographic record
Abstract
Plusieurs écoles tentent d'innover dans un contexte où le \npaysage technologique évolue rapidement et les tablettes numériques telles l'iPad sont clairement identifiées comme une technologie émergente susceptible d'avoir des retombées importantes en éducation à très court terme (Johnson et al., 2012). Une école secondaire québécoise intègre \ndepuis septembre 2012 des iPad dans deux de ses groupes de troisième secondaire. Une équipe de recherche accompagne l'école et suit leur parcours. Cet article présente un premier regard sur les données préliminaires amassées depuis septembre 2012 auprès des enseignants, des élèves et de leurs parents. \n \nMany schools are trying to innovate while the technological \nlandscape is changing rapidly. Digital tablets like the iPad are clearly identified as an emerging technology that could have a significant impact on education in the short term (Johnson et al., 2012). A Quebec high school \nintegrated iPad in two secondary three groups since last September. A research team accompanied the school and followed its course. This paper presents a first look at the preliminary data collected since the beginning of the project from the teachers, the students and their parents.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
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".