Les technologies immersives en formation : révolution numérique stratégique ou dispositifs parmi d’autres?
Bibliographic record
Abstract
L’augmentation des investissements et du nombre de travaux de recherche sur les technologies immersives laisse penser qu’il s’agit de révolutions pour lesquelles les chercheurs ont tout intérêt à se positionner pour apporter des solutions à la société. À travers cette mouvance, différentes applications des technologies immersives en formation sont identifiées, de même que les scénarios pédagogiques associés. Cependant, de nombreux travaux démontrent les apports de ces technologies, oubliant quelquefois qu’il ne s’agit que d’outils. Notre contribution a pour but d’interpeler sur la nécessité d’apporter une complémentarité entre les dispositifs existants plutôt que d’en creuser en se concentrant sur certains d’entre eux; car malgré les avantages des technologies immersives, il existe également des limites qui peuvent être compensées par des moyens traditionnels ou moins immersifs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".