Les méthodes d’évaluation utilisées à l’ordre d’enseignement universitaire dans les cours en administration des affaires
Bibliographic record
Abstract
L’Association to Advance Collegiate Schools of Business (AACSB) incite fortement les facultés d’administration des affaires à la mise en place des standards de l’Assurance of Learning. Selon ces standards, les professeurs doivent s’assurer qu’un apprentissage a lieu pour chacun des buts des programmes (qui doivent refléter les compétences attendues sur le marché du travail). Afin de mesurer le degré d’atteinte de ces buts, une diversité de méthodes d’évaluation est utilisée. À ce jour, très peu d’études ont porté sur l’opinion des étudiants face à l’utilisation de ces méthodes d’évaluation (attrait, perception d’efficacité, équité, anxiété générée, niveau cognitif mobilisé). Cette étude consiste à évaluer ces différents aspects (n = 95). L’analyse des résultats indique que les étudiants possèdent une opinion différente selon la méthode d’évaluation considérée. La discussion porte sur l’importance d’utiliser ces méthodes d’évaluation pour répondre aux standards de l’AACSB.
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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 | 0.200 | 0.306 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".