ENGINEERING DESIGN IN THE CREATIVE AGE
Bibliographic record
Abstract
McMaster University has initiated a new graduate program in engineering practice aimed at educating tomorrow’s engineering design leaders. Graduates of engineering schools are well versed in technology and its application but must acquire new skills and competencies in innovation and design in order to become global leaders in their industries. The leading thinkers in engineering design innovate continuously to succeed in the global marketplace. This paper discusses the value and importance of teaching and learning human-centred design thinking for engineering graduates. Achieving significant and continuous innovation through design requires looking beyond current systems design practices. Engineering educators must adapt new ways of thinking, teaching, and learning engineering design from other disciplines. This paper discusses the modes of engineering thinking and how they differ from those of contemporary innovators and examines how a human-centred approach to design can replace approaches that consider human values and ethics as constraints to the design. The authors will discuss current efforts to insert the teaching and learning of a human-centred approach to engineering design at the graduate level in an engineering curriculum. The aim of the curriculum is to introduce students to collaborative, inter-disciplinary, human-centred thinking, with a strong emphasis on generating continuous innovation through creativity.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".