Project-Based Learning in Engineering: Illustration by a Capstone Project of an Electric Vehicle
Bibliographic record
Abstract
The Department of Electrical and Computer Engineering of the Université de Sherbrooke has developed an innovative learning approach qualified as Problem- and Project-Based learning approach (PPBL), which is applied during all engineering studies. In this paper, we concentrate on capstone projects by first presenting the general organization, activities, deliverables and assessment of these projects. We detail the team academic activities, starting from choosing the project until the post-mortem report, and we give an idea of the students autonomy to learn and acquire new competencies. Then to illustrate the development of a capstone project, we consider a specific project related to an enhancement of an electric vehicle prototype. The latter has two main objectives: to reduce battery aging and optimize energy consumption of an electric vehicle during its trips. It is a multi-disciplinary project which includes electrical, electronic and software development competencies. The project was done in 8 months (two terms), by a team of 9 students, working more than 2 days a week.
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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 | 0.003 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".