An approach for improving design and innovation skills in engineering education: the multidisciplinary design stream
Bibliographic record
Abstract
Engineering practice is multidisciplinary by nature. While some engineering projects may require discipline-specific specialists, the vast majority of engineering practice is carried out either by an engineering team of mixed disciplines, or by individual engineers who are competent across multiple fields. In both Canada and the US, engineering accreditation boards have recognized the need for students to develop at least a modest level of competency to function in multidisciplinary teams prior to graduation. Recognizing the growing need for enhanced design education and multidisciplinary competency for undergraduate students, in 2005 Queen’s University introduced an elective series of courses known as the Multidisciplinary Design Stream (MDS), available to students from all engineering disciplines. The first course in the stream is offered over one term at the third year level and incorporates a broad range of lecture topics and interactive learning activities that are further reinforced with a concurrent design project in multidisciplinary teams of four students. The continuing course spans the final two terms at the fourth year level and enhances students’ design, professional, and problem solving skills through their application in multidisciplinary teams on funded, industrysponsored projects. Every team is supervised by one or more faculty members or ‘engineers in residence’, all of whom have significant engineering professional practice experience. The MDS has been filled to capacity since its second year of operation. Student feedback after graduation is very positive, and client response has typically been outstanding, reinforced with a very high rate of year over year client return. Student surveys and a design skills assessment provide significant evidence of increased design competency.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".