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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".