Les enjeux des plateformes numériques d'enseignement gamifiées : Enquête d’utilisation de la plateforme Pix
Bibliographic record
Abstract
Depuis 2019, les « compétences numériques » dans les cursus scolaire et universitaire français sont enseignées au moyen de la plateforme en ligne Pix. L’étude de cette plateforme et de ses caractéristiques relevant de la gamification permet d’interroger les enjeux de ces nouvelles formes d’enseignement. Nous avons procédé à une enquête menée auprès de deux cohortes d’étudiants afin d’évaluer leurs modalités d’appropriation et d’observer leurs résultats, que nous avons pu comparer à des résultats en enseignement traditionnel. Les résultats de notre enquête permettent d’analyser les enjeux portés par la plateformisation gamifiée de l’enseignement dans la modification des contenus pédagogiques, dans la relation à l’autorité enseignante et dans la nouvelle posture apprenante.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".