Interdisciplinary Capstone Engineering Projects for Medical Technologies Design: Cross Discipline Design and Communication Challenges
Bibliographic record
Abstract
This paper reports observations made during a 5-year implementation of Interdisciplinary Capstone Engineering Projects for Medical Technologies Design at McGill University between 2019-2023. This effort was realized in the context of a NSERC Engineering Design Chair for unifying the capstone projects across all departments in the Faculty of Engineering in order to strengthen the diversity of students design training. Students experience, examples of interdisciplinary training, development of assessment methods and industrial sponsors feedback are reported. It was observed that interdisciplinary teams acquired the Design graduate attributes required by the Canadian accreditation board based on graded work and were able to overcome the communication barriers. Various design and communication activities were assessed including conceptual design, design embodiment, design prototypes and testing and reports writing, oral presentations, team meetings. Students were also required to make a design notebook logging their concepts in the form of sketches, minutes of meetings, with concept generation and selection methodologies. Metrics needed to be developed for the different accreditation criteria, questionnaire were elaborated for the sponsors and surveys were implemented for assessing current students and alumni training satisfaction level. The results are summarized with histograms, tables and percentages. A comparison between prior implementation period and post implementation period is made. The results are analyzed in terms of Design training proficiency, taking into account the skills acquired for designing, manufacturing and testing. The sponsors information reported the appreciations of individual contribution, incorporation of clients needs, team's communication with the client project and team's achievement of client objectives. It was observed that the students were globally interested by the multidisciplinary projects. It is concluded that the teams involved with the design of Medical Technologies were successful in achieving the assigned design projects and were able to overcome the communication challenges linked to the different engineering disciplines. This resulted in excellent academic accomplishments, working prototypes and appreciation by the sponsors.
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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 | 0.049 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".