TOWARDS EFFECTIVE MULTIDISCIPLINARY ENGINEERING EDUCATION: THE MULTIDISCIPLINARY DESIGN STREAM AT QUEEN'S UNIVERSITY
Bibliographic record
Abstract
Professional engineers in industry not only have to work frequently with those from other disciplines and professions, but often have to develop working skills and knowledge beyond their original discipline due to the requirements of their employment. Similarly, engineering design skills are also important attributes for professional engineers, particularly those working in product, process or system development. Surveys continue to suggest that industry perceives most engineering graduates, although technically competent, have minimal practical design skills, and lack the complimentary professional skills that are required for working successfully in the shared workplace. In an effort to address the need for both multidisciplinary and design engineering skills, a multidisciplinary design stream is under development at Queen’s University. Beginning with a course designed to develop a broad range of fundamental engineering design knowledge, professional skills and attitudes, the stream will continue to enhance the student’s capability through a full year experience working on industry based design projects in multidisciplinary teams. The first elective offering of the design engineering fundamentals course attracted students from nine of ten disciplines. The project phase of the stream will be first offered in 2005-2006 to those students completing the fundamentals course. This paper will therefore discuss the multidisciplinary design stream as a work in progress.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".