Des évaluations éducatives par le jeu
Bibliographic record
Abstract
Ce chapitre examine comment les progrès récents de la technologie numérique pourraient conduire à une nouvelle génération d’évaluations éducatives par le jeu. Les systèmes d’éducation disposeraient alors d’évaluations capables de tester des compétences plus complexes que les tests standardisés classiques. Après avoir souligné certains des avantages des évaluations par le jeu par rapport aux autres tests, ce chapitre aborde la manière dont ces tests sont construits, comment ils fonctionnent, mais aussi certaines de leurs limites. Si les jeux présentent un grand potentiel pour améliorer la qualité des tests et étendre l’évaluation à des compétences complexes à l’avenir, ils viendront probablement compléter les tests classiques, qui ont aussi leurs avantages. Trois exemples d’évaluations par le jeu qui intègrent des technologies avancées illustrent cette perspective.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".