Les modalités d’évaluation des apprentissages à l’enseignement supérieur : L’apport de la conception universelle de l’apprentissage (CUA) dans un contexte inclusif d’enseignement à distance
Bibliographic record
Abstract
La variabilité des apprenants fréquentant les établissements universitaires et l’essor de la formation en ligne en situation de crise sanitaire amènent les enseignants à se questionner sur leurs pratiques. Relativement à l’évaluation des apprentissages, des questions se posent concernant les différentes pratiques visant à réduire les inégalités. Le dispositif de formation présenté dans cet article propose une façon de favoriser des pratiques pédagogiques actives dans un contexte en ligne et de penser l’évaluation de manière inclusive afin de favoriser les apprentissages et une évaluation équitable. Cette étude de cas expose la démarche effectuée dans le cadre d’un cours au cycle supérieur en sciences de l’éducation offert à un groupe de douze étudiants provenant de milieux différents et ayant des expériences éclectiques.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".