Analyse des stratégies d’apprentissage dans une méthode d’apprentissage par problèmes : le cas d’étudiantes en soins infirmiers
Bibliographic record
Abstract
Plusieurs institutions d’enseignement supérieur ont adopté l’apprentissage par problèmes comme méthode pédagogique, mais peu d’études, à notre connaissance, se sont intéressées à décrire les stratégies utilisées par les étudiants dans un curriculum d’apprentissage par problèmes. Le but de cette recherche est justement l’étude de ces stratégies. L’analyse des verbatims obtenus auprès de 31 étudiantes1 de soins infirmiers a permis de dégager un ensemble de stratégies et d’en évaluer la fréquence. Les résultats suggèrent que les étudiantes utilisent davantage des stratégies de traitement en surface de l’information que des stratégies de traitement en profondeur malgré des indications voulant que l’utilisation d’une approche en profondeur soit associée à une meilleure réussite au cours.
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 | 0.017 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".