Bibliographic record
Abstract
While there are numerous studies documenting the skills and abilities of experienced designers and engineers, research is needed to document the specific practices or behaviors of design engineers, a subset of creative engineers who solve complex problems. To document observed practices of design engineers, twelve experienced engineers were asked to describe an expert design engineer, someone who always has the solution when others do not. Using inductive thematic analysis, nine observed practices with 30 subtopics were identified from 186 data points. The observed practices of design engineers include being collaborative, confident, creative, independent, intuitive, inquisitive, motivated, systematic, and versatile. Eight additional data points document varying observations of design engineers' interest in mentoring or management. While participants spoke with reverence about the design engineers, some observed practices could have a negative connotation, such as being egotistical, conservative to a fault, and not good at public speaking. One realization from this paper is that studies generally report admirable practices to replicate, when potentially negative practices can help engineering educators to better prepare students for industry. Lastly, this article provides engineering educators with a mapping between the observed practices of design engineers and the graduate attributes used in accrediting Canadian engineering programs.
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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".