Enquête sur les facteurs qui motivent les étudiants à participer aux compétitions d'ingénierie | Survey on Motivational Factors Driving Student Participation in Engineering Competitions
Bibliographic record
Abstract
Cette étude explore les facteurs qui motivent les étudiant·e·s en génie à participer aux compétitions académiques. À travers une analyse quantitative et qualitative basée sur un sondage auprès d’étudiant·e·s du Québec et de l’Est du Canada, la recherche identifie les éléments clés influençant leur engagement. Les résultats montrent que la motivation est principalement intrinsèque, liée à l’apprentissage collaboratif, au développement des compétences disciplinaires et à la curiosité intellectuelle. L’appartenance à une communauté académique et la stimulation intellectuelle sont des moteurs essentiels de l’engagement étudiant. Contrairement aux attentes initiales, le fait de « penser et agir comme un ingénieur » n’est pas un facteur prioritaire. Les étudiant·e·s privilégient plutôt le défi intellectuel, la créativité et le travail en équipe. Les motivations extrinsèques, telles que la reconnaissance académique et les opportunités de carrière, bien que présentes, jouent un rôle secondaire. L’étude souligne l’importance d’adapter les approches pédagogiques en misant sur l’apprentissage expérientiel et collaboratif. Les compétitions de génie, en favorisant un environnement stimulant et interactif, constituent un levier puissant pour renforcer l’engagement et la persévérance des étudiant·e·s, préparant efficacement à leur future carrière. This study explores the factors that motivate engineering students to participate in academic competitions. Through a quantitative and qualitative analysis based on a survey conducted among students from Quebec and Eastern Canada, the research identifies key elements influencing their engagement. The results show that motivation is primarily intrinsic, driven by collaborative learning, the development of disciplinary skills, and intellectual curiosity. A sense of belonging to an academic community and intellectual stimulation are essential drivers of student engagement. Contrary to initial expectations, “thinking and acting like an engineer” is not a primary motivational factor. Instead, students prioritize intellectual challenges, creativity, and teamwork. Extrinsic motivations, such as academic recognition and career opportunities, while present, play a secondary role. The study highlights the importance of adapting pedagogical approaches by focusing on experiential and collaborative learning. Engineering competitions, by fostering a stimulating and interactive environment, serve as a powerful lever to enhance student engagement and perseverance, effectively preparing them for their future careers.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".