11. Dossier de Progression et Dossier de Réussite: Deux Outils Complémentaires au Service de la Professionnalisation Enseignante dans le Supérieur
Bibliographic record
Abstract
Cet article aborde et illustre les notions de référentiel de compétences, de dossier de progression et de dossier de réussite (Tardif, 2006) tels qu’ils sont mis en oeuvre au sein du programme Formasup, master complémentaire en pédagogie de l’enseignement supérieur organisé à l’université de Liège. Les auteurs présentent l’articulation interne de ce programme qui cible un nombre limité de compétences critiques, accompagne le développement de ces compétences chez les enseignants participants et mesure ce développement à travers un portfolio. Ils discutent enfin des défis et difficultés qui persistent ainsi que des moyens mis en place par l’équipe encadrante pour tenter de les surmonter.
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 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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".