TEACHING WISDOM AND OTHER SOFT SKILLS WITHIN ENGINEERING CURRICULA
Bibliographic record
Abstract
Engineering accreditation bodies routinelyexamine the state of university engineering programs toensure currency and relevance. Accreditation by theCanadian Engineering Accreditation Board (CEAB)focuses largely on the development of technical skills andcompetencies. While required graduate attributesacknowledge the inclusion of selected “soft skills”, e.g.communications and teamwork, curricular emphasis leansdecidedly in the direction of achieving technical skillsimplying that soft skill development is squeezed in as anafterthought rather than being afforded deliberaterecognition. Indeed, rapid growth of technologicaldevelopment as well as including content required byregulatory agencies (e.g. health and safety), points towardeven greater pressure to marginalize soft skills, whichparadoxically, seasoned engineering managers look for intheir hires and those considered for promotion.In addition to basic communications and teamwork,important soft skills and competencies include: creativity,collaboration, instilment of a sense of wonder/curiosity,learning to learn, lifelong learning, reading withcomprehension, thinking skills, and the infusion of wisdomto design, problem solving and decision making.Including soft skills development presents a challenge formost engineering professors, often because their owneducation was focused almost exclusively on technicalmaterial. Given this situation and evolving curricularpressures, the challenge becomes identifying ways andmeans of introducing the teaching of wisdom toengineering students.This paper focuses on one particular soft skill: wisdom, aconcept which can be difficult even to define, let aloneconvey/teach. Engineering professors must think throughwhat is meant by wisdom, structure opportunities for theconsideration of wisdom in design/decision makingsituations and develop methods for evaluating theapplication of wisdom – all within existing curricularconstraints. Practical suggestions are advanced to helpengineering professors infuse wisdom into their lectures,tutorials and labs as a matter of accelerating the learningand maturation of their students.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".