Théories de l'apprentissage mobile à la lumière de l'étude de la mobilité et de la pensée complexe
Bibliographic record
Abstract
En 2013, l'utilisation des périphériques informatiques mobiles surpassait celle de l'ordinateur de bureau en ce qui concerne l'accès à Internet par la population mondiale. Ce phénomène a rapidement donné naissance à une pléiade de nouvelles pratiques sociales et culturelles et ce sont principalement les jeunes qui en sont les instigateurs. Cette situation met en lumière le besoin pour le domaine de l'éducation de développer une compréhension théorique du phénomène de l'apprentissage mobile, car ce ne sont pas tant les technologies qui sont nouvelles que la mobilité de l'apprenant, des informations, des contextes et des systèmes d'apprentissage. Ce court article de recherche documentaire présente les développements théoriques récents de ce nouveau paradigme pour les sciences éducatives en le mettant en perspectives avec les champs des sciences sociales de l'étude de la mobilité et de la pensée complexe.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".