Critical Thinking to Creative Problem-Solving in Engineering and Management
Bibliographic record
Abstract
Organizations advance and grow by solving problems one at a time, and management graduates should possess critical thinking and problem-solving capacities to be effective business managers and creative engineers. The problem was that critical thinking and creative problem solving are operationally ill-defined in engineering and management education. The purpose of this grounded theory study was to discover how, if at all, the engineering and management scholar-practitioner community in British Columbia, Canada, perceives the concepts of critical thinking and creative problem-solving. A three-prong critical thinking conceptual framework was used as template for defining and relating the two key concepts of the study. Research questions asked about operational definitions of critical thinking and creative problem solving and any relationships between them. Data were collected by interviewing eleven participants, with experience as educators and practitioners, and thematized into concepts for developing a theory that describes the perceived meanings of critical thinking and its relation to problem-solving. Findings included that employer’s expectations can be better met through critical thinking employees’ contributions to find the right problems and solve, or manage, them effectively. Finally, it was illustrated that positive social change ensued from improving the critical thinking and problem-solving capacity of graduates, as they support to their organizations in delivering products and services of value to elevate the living standards of society at large.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.074 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".