L’utilisation des diagrammes logiques dans la construction des hiérarchies d’apprentissage
Bibliographic record
Abstract
La construction de hiérarchies d’apprentissage constitue une étape importante de la planification de systèmes pour l’enseignement d’habiletés intellectuelles. Plusieurs techniques ont été proposées jusqu’à présent. Ces techniques offrent de nombreux avantages mais présentent quelques inconvénients dont un manque de précision quant à la façon de conduire le processus et une faiblesse quant à la possibilité de produire une liste exhaustive des capacités qui auront à être enseignées. La technique qui sera décrite dans les lignes qui suivent tente de réunir les avantages et éviter les inconvénients de ces dernières techniques. La procédure repose fondamentalement sur la construction de diagrammes logiques tels qu’utilisés en informatique et sur le concept de hiérarchies d’apprentissage tel que proposé par Robert M. Gagné.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".