Quand l’apprentissage collaboratif devient un outil puissant de motivation, de découverte et d’évaluation sommative
Bibliographic record
Abstract
Pour que l’alignement pédagogique soit constructif, l’enseignant dans son cours devrait rechercher la cohérence entre les apprentissages attendus, les activités d’enseignement et les évaluations. Si d’un côté les programmes de génie montrent une tendance générale vers une approche par compétence, dictée entre autres par l’agrément des programmes, l’évaluation sommative des cours est encore assez traditionnelle, constituée souvent par la résolution numérique d’exercices dans un examen. Il est évident que telle forme d’évaluation n’est pas cohérente avec la philosophie suggérée par une approche par compétence, ne permettant pas d’apprécier correctement la capacité des étudiants de mobiliser les différents apprentissages dans la résolution d’un problème ouvert. Cet article veut relater l’expérience positive de l’approche par projet sur la motivation et l’apprentissage des étudiants de deux cours du cheminement critique, en montrant que les formes d’évaluation traditionnelles peuvent être remplacées par d’autres formes d’évaluation, sans nécessairement nuire à la formation des étudiants. [English version] For pedagogical alignment to be constructive, the teacher should seek coherence between expected learning, teaching activities and assessments. If on the one hand engineering programs show a general tendency towards a competency-based approach, dictated among other things by the accreditation of programs, the summative evaluation of courses is still quite traditional, often consisting of the numerical resolution of exercises in an exam. It is obvious that such a form of evaluation is not consistent with the philosophy suggested by a competency-based approach, making it impossible to correctly assess the students' ability to mobilize different learning in solving an open problem. This article aims to report the positive experience of the project approach on the motivation and learning of students in two critical path courses, showing that traditional forms of evaluation can be replaced by other forms of evaluation, without necessarily harming the students’ learning.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".