Reinventing Design Learning in Bioengineering with Authenticity and Project-Based Learning
Bibliographic record
Abstract
The course BGM720 – Conception en bio-ingénierie is pivotal in the bioengineering pathways of four engineering programs at Université de Sherbrooke (UdeS). However, it faced several challenges, such as complex and abstract content that hindered student engagement, and limited use of formative assessments for self-improvement. To address these challenges, we used a project-based learning (PjBL) approach. We revamped the existing design project and its assessment procedures to make them more authentic. In fall 2024, students were asked to design a myoelectric hand prosthesis as a team, following the procedures specific to the medical device field. Design history files and design reviews served as assessments. We evaluated the changes mainly through questionnaires. The vast majority of students perceived they achieved training objectives, appreciated PjBL, engaged in the design project, and found the assessment relevant. Bringing the design project and its evaluation closer to authentic practices helped us mitigate the challenges we encountered.
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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 | 0.050 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".