TOWARDS EFFECTIVE MULTIDISCIPLINARY ENGINEERING EDUCATION: THE MULTIDISCIPLINARY DESIGN STREAM AT QUEEN'S UNIVERSITY - PART II
Bibliographic record
Abstract
Professional engineers 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. Engineering accreditation bodies have accordingly begun to include the ability to function in multidisciplinary teams as a demonstrable requirement for accreditation of engineering schools. Similarly, engineering design skills are also important attributes for professional engineers, particularly those working in product, process or system development. Although long required by many engineering accreditation bodies, it is perceived by industry that most engineering graduates, although technically competent, have minimal practical design skills. There are many factors in most Canadian engineering schools that may limit student’s development of multidisciplinary and design skills. These include separate engineering departments, departmental funding policies, schedules based on individual disciplines, and heavy core course loads based on the perceived need for more math, science and computer courses. As a result, practical design experiences may be limited to one final year, discipline specific course. In an effort to address the need for both multidisciplinary and design engineering skills, a Multidisciplinary Design Stream has been developed at Queen’s University. Beginning with a course designed to develop a broad range of fundamental engineering design knowledge, skills and attitudes, the stream culminates with a full academic year experience working on industry based design projects in multidisciplinary teams. The first paper in this series, presented at the 2005 CDEN conference, discussed the first course in this stream and laid out the plans for the successive multidisciplinary industry-based design project. This paper will extend that discussion to review the industry project phase and reflect on the overall results of the first full offering of the multidisciplinary design stream.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".