L’hybridation pédagogique à l’université marocaine face aux différents profils et besoins des personnes étudiantes : place à la différenciation et à l’évaluation
Bibliographic record
Abstract
Notre étude, basée sur le modèle ADDIE, vise à analyser les besoins de la population étudiante marocaine pour l’intégration de l’apprentissage hybride dans leur processus éducatif. Une enquête en ligne a été réalisée afin de recueillir un large éventail de réponses. Les résultats révèlent des différences selon les spécialités (sciences humaines, sciences et technologies, gestion) et confirment les hypothèses initiales. Des corrélations statistiques montrent des liens entre les caractéristiques des personnes étudiantes et leur niveau de compétences numériques. Ces conclusions suggèrent l›adoption d›une pédagogie différenciée dans l›enseignement hybride, basée sur l’utilisation des plateformes d’apprentissage, pour mieux répondre aux besoins des apprenantes et des apprenants.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".