Experiential Learning for First Year Engineering through Outreach with a Local Community Hospital
Bibliographic record
Abstract
The lecture is the staple of university learning. It can be effective in the teaching of fundamental engineering subjects such as calculus and physics. However, it offers little in bringing students to understand the role of the engineering profession and its practice. With limited budgets, time and resources, and large first year classes, creating a poignant learning experience for our students is immensely challenging. Yet the university classroom offers an unparalleled opportunity given its diverse pool of talent, skill, and ambition. With guidance our students have the ability to not only meet our high academic expectations but also make a real difference for people in their local communities. At McMaster University, our entire first year engineering cohort is contributing to their local community through interactions with medical professionals and their clients. With an increased emphasis on design, experiential learning, and community outreach we at McMaster University have developed a unique first year course that involves the design of rehabilitation devices in collaboration with occupational therapists, researchers, industry professionals, and local volunteers. The project provides students with a rich and real-world practice experience as well as benefiting those clients with unique problems. This paper describes the successful collaboration between the Hand Therapy Clinic at Hamilton General Hospital and the Engineering 1 programme at McMaster University. The occupational therapists at the clinic presented 800+ first year engineering students with a challenge: design a dynamic elbow orthosis for the unique problems faced by two of their clients. Volunteers from the local community of occupational therapists, industry professionals, and biomedical researchers worked with the students to help them design, build, and test their designs. The paper highlights the challenges of teaching the large class using experiential learning and outlines the approaches for challenging students with unique problems that demand creativity.
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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 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.008 |
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".