Incorporating Critical Thinking into an Engineering Undergraduate Learning Environment
Bibliographic record
Abstract
Critical thinking extends to all aspects of professional engineering, especially in technical development, and, since the introduction of the ABET 2000 criteria, there has been an increased emphasis in engineering education on the development of critical thinking skills. What is hoped for is that the students obtain critical thinking skills to complement their abundant logical thinking skills. Critical thinking is a developmental skill that cannot be taught simply by traditional methods, i.e., step-by-step instruction followed by repetitive skills, as used for logical thinking skills. The research and development reported here is concerned with outlining how students first can be alerted to what critical thinking is, and how critical thinking can be nurtured through practical experience with appropriate guidance and reinforcement. The module ‘Introduction to Fluid Mechanics’ has been selected to demonstrate how formal critical thinking components may be introduced with the development of principles of logic and an obligatory problem solving procedure. A method of assessment during various phases of critical thinking, i.e., initial design or set-up, testing of method or prototype, and evaluation of the results, of open-ended assignments in engineering for each year of the undergraduate course has been developed.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".