Problem- and Project-Based Learning in Engineering: A Focus on Electrical Vehicles
Bibliographic record
Abstract
An innovative learning approach, qualified as Problem- and Project-Based Learning (PPBL), has been developed in the Department of Electrical and Computer Engineering of the Universite de Sherbrooke. PPBL is applied totally from the first term of our programs, instead of being applied gradually. Basically, the students are involved in two types of activities in each term. The first one consists of several (typically, six) consecutive Problem- Based Learning (PBL)-units, where each PBL-unit lasts generally two weeks and is focused around the resolution of an engineering problem. The second type of activities is to realize a project throughout the term, which requires the resolution of a more complex technical problem and the use of project management methods. The experience and learning obtained in solving the small consecutive engineering problems should be used in the resolution of the project. After a presentation of how PPBL is implemented in our department, its benefits are highlighted in an area that is becoming increasingly important: Electrical vehicles. We present in particular several capstone projects realized by our students that have led to the production of prototypes of electrical vehicles. Several prototypes have obtained prices in international competitions or will participate in competitions in a near future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".