Enseigner la théorie des mécanismes selon une approche par projet : un exemple pratique | Teaching the Theory of Mechanisms through a Project-Based Approach: A Practical Example
Bibliographic record
Abstract
L’enseignement traditionnel en génie mécanique, axé sur des cours magistraux et des exercices dirigés, limite l’engagement des étudiant(e)s et leur capacité à appliquer concrètement les concepts enseignés. De plus, l’évaluation repose souvent sur des examens basés sur la ésolution numérique de problèmes, ce qui ne reflète pas pleinement les principes de l’apprentissage par compétences. Cette étude explore l’intégration d’une approche par projet dans un cours de théorie des mécanismes, où les étudiant(e)s appliquent les notions théoriques à un problème d’ingénierie authentique. Les modifications apportées à la séquence d’enseignement et aux moyens d’évaluation sont détaillées, mettant en évidence la possibilité de repenser les évaluations traditionnelles. L’analyse des commentaires des étudiant(e)s révèle des effets positifs sur leur motivation, engagement et capacité à établir des liens entre la théorie et la pratique en ingénierie. Traditional teaching in mechanical engineering, focused on lectures and guided exercises, limits student engagement and their ability to apply concepts concretely. Moreover, assessment often relies on exams based on numerical problem-solving, which does not fully align with the principles of competency-based learning. This study explores the integration of a project-based approach in a theory of mechanisms course, where students apply theoretical concepts to an authentic engineering problem. The modifications made to the teaching sequence and assessment methods are detailed, highlighting the possibility of rethinking traditional evaluations. The analysis of student feedback reveals positive effects on their motivation, engagement, and ability to establish connections between theoretical concepts and engineering practice.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 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".