Le MOOC et le « hype » : analyse critique des discours médiatiques sur les MOOC.
Bibliographic record
Abstract
Texte de réflexion critique RésuméEn un court laps de temps, les MOOC ont suscité de vives attentes suivies de grandes désillusions dans les discours médiatiques, ce qui apparait typique du déterminisme technologique avec lequel est souvent abordé le domaine du numérique en éducation.Dans ce contexte, l'objectif du présent article est d'analyser, dans une perspective critique, les discours d'articles journalistiques généralistes en ligne sur les MOOC.Une analyse de contenu nous a permis de caractériser les discours journalistiques et d'évaluer la part de déterminisme technologique dont ils ont fait l'objet dans leur traitement médiatique.Nous en tirons finalement quelques conclusions sur la manière dont sont appréhendées socialement les innovations numériques en éducation.
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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 | 0.010 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".