An approach for improving design and innovation skills in engineering education: the multidisciplinary design stream
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it