Lessons Learned from Teaching a Pilot Multidisciplinary Entrepreneurial 4th Year Capstone Design Course
Bibliographic record
Abstract
During the 2015/16 academic year, a pilot course at the University of Saskatchewan was offered to senior engineering students. The pilot course was meant to offer an entrepreneurial version of the standard 4th year capstone design course. It also created an opportunity for students to work with students from engineering disciplines other than their own. Two design groups, each consisting of four students, were formed. This paper describes the structure of the course, how the entrepreneurial content and multidisciplinary aspects were handled, and a variety of lessons that were learned that may be of value to other institutions considering similar ventures.The College’s capstone design courses had the weightings of two regular 3-credit courses, running from the start of the Fall term to the end of the Winter term. The most fundamental differences between this course and the standard 4th year capstone course were i) the students identified their own design problem, and ii) they formed multidisciplinary teams to solve their problem. Both of these differences created significant challenges in terms of organizing and running the course. Students from Electrical Engineering, Computer Engineering, and Engineering Physics were full participants in the course. Students from Mechanical Engineering were given the opportunity to participate on a one course credit basis i.e. they still had to take the standard 4th year design course in addition to the entrepreneurial version.Many lessons have been learned from the experience of developing and teaching this course. Issues that will be discussed in the paper include, but will not be limited to: integrating the different learning outcome needs of the different departments involved, managing the uncertainty of the design problems undertaken, integrating entrepreneurship into the design course, talking about design to students from different disciplines, managing “sub-contractor” students in capstone projects, evaluation, scheduling of classes, multidisciplinary supervision, client interaction and evaluation of student work, peer assessment, and student group dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".