A Customizable Multidisciplinary Design Program in a Traditional Engineering Faculty
Bibliographic record
Abstract
A new three-year Bachelor of Multidisciplinary Design – Experiential Learning program was approved by the Ontario Ministry of Colleges and Universities in 2022 at the University of Ottawa in Canada. The program is situated in the Faculty of Engineering and welcomed its first student cohort in the Fall of 2023. The program is designed for students who have a diverse set of interests, and who are passionate about combining technology with other fields, including social science, business, or arts, rather than those wanting to be engineers. The flexible program provides students with the skills required for modern multidisciplinary job markets, and with the opportunity to define and pursue their own career trajectory. As this type of flexibility and openness may seem daunting for first year students, sample learning paths were created based on current job market trends, with more learning paths in development. To support using these paths and developing new ones, a tool was developed to help students plan their path and select their courses. This paper focuses on the development of this unique program in the Canadian context, as well as the challenges associated with its development. Since this new program is not a traditional engineering discipline, and is targeting non-traditional students, recruitment efforts and marketing needed to be modified from those used elsewhere in the Faculty.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 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".