Les natifs du numérique aux études : enjeux et pratiques | The Digital Natives in Education: Issues and Practices
Bibliographic record
Abstract
Largement débattu dans la littérature, il existe tout un débat autour des générations dans la société. Souvent utilisé pour caractériser les individus, les réflexions basées sur les générations peuvent devenir problématique lorsque les décideurs orientent leur décision sur des théories non fondées empiriquement. Cette étude propose d’examiner les natifs du numérique à partir de données empiriques, sous la perspective des usages du numériques en éducation. Nos résultats permettent de nuancer ce que l’on croit connaitre des natifs du numérique tout en appuyant d’autres études menées à travers le monde, qui mettent en exergue les usages technologiques et le numérique éducatif. Although widely discussed in the public media, there is currently a debate about the characteristics of generations in society (C, X, Y, Z), particularly with regard to their technological habits. Based on 24, 502 college students, this study proposes to examine the digital natives in terms of their use of technologies in education. The results of multivariate analysis challenge our assumptions about digital natives while supporting other studies from around the world that highlight technological uses and educational potential.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".