TRAINING VERSATILE ENGINEERS: A HISTORICAL AND PRESENT PERSPECTIVE ON THE PLACE OF THE HUMANITES AND SOCIAL SCIENCES IN THE CANADIAN ENGINEERING CONTEXT
Bibliographic record
Abstract
The importance of training well-rounded engineers has been discussed by engineering educators since the end of the Second World War. For decades now, the humanities and social sciences have been used to encourage engineering students to develop social competency, ethical awareness, and the ability to express themselves with ease, both orally and in writing. In Canada, the humanities and social sciences are featured prominently in the curriculum as part of complementary studies, which comprises both required and elective courses. How do students understand their experience with the humanities and social sciences during their degree? Do they see the usefulness of the skills and content learned in these fields for the job market? This study constitutes a first step in a larger project exploring these questions. Here we first present an overview of the historical and present debates on the place that humanities and social sciences have in the engineering curriculum. We then report on the feedback obtained from focus groups of graduating students asked about their experience and attitudes relative to “soft skills” graduate attributes and complementary studies. We conclude that the new Canadian Engineering Accreditation Board graduate attribute framework provides an opportunity to assess the role of the humanities and social sciences in the engineering curriculum and suggest possible ways to measurably enhance student experience and learning of non-technical or “soft” skills.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.024 | 0.022 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".