Le développement de compétences numériques dans des environnements d'apprentissage riches en technologies
Bibliographic record
Abstract
Au 21e siècle, l'acquisition de compétences numériques est importante afin de rester à jour avec les avancements technologiques qui affectent la vie quotidienne. Cherchant à mieux comprendre l'acquisition des compétences numériques, ce projet de recherche vise les environnements d’apprentissage riches en technologies, plus précisément les laboratoires de fabrication numérique. Nous avons réalisé une étude de cas multiples dans 4 écoles dans la province canadienne du Nouveau‑Brunswick (N.-B.) où nous avons capturé un total de 23 vidéos d’élèves au travail dans des laboratoires de fabrication numérique. Les résultats démontrent que la pensée critique, la créativité, la collaboration, la communication et la résolution de problèmes sont mises en évidence dans les laboratoires de fabrication numérique.
 In the 21st century, many school systems are turning to the development of skills as an educational goal, including digital skills. However, the current scientific literature on digital skills remains insufficient, both in terms of their definition and the processes of their development. Our research project aims to examine the presence of digital skills in learning environments that are considered technology-rich, specifically makerspaces. We conducted a multiple case study in three schools in New Brunswick where we observed students in the process of working on a project in a makerspace setting, and our analysis focused on the digital skills demonstrated. The results suggest that the type of activities that young people do in a makerspace, as well their age and the time they spend in the makerspace, can all influence the development of digital skills.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".