Sentiment d’efficacité personnelle, apprentissages autorégulés et expérience dans un contexte de formation hybride universitaire
Bibliographic record
Abstract
Cet article traite de la relation entre le sentiment d'efficacité personnelle (SEP), les apprentissages autorégulés (AAR) et l’expérience du contexte de formation hybride à l'Université Paris Cité. L'étude, motivée par l’hybridation de la formation due à l'augmentation du nombre d'étudiants et les défis logistiques, explore la corrélation entre ces deux variables et l’expérience des étudiants en contexte de formation hybride. En utilisant une approche quantitative, nous avons analysé les réponses de 110 étudiants, concluant que le SEP et l’AAR évoluent en fonction de l’expérience des étudiants, suggérant des implications importantes pour l'optimisation des stratégies pédagogiques dans des environnements hybrides.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".