REFLECTING ON THE USE OF DESIGN METHODOLOGY IN ENGINEERING DESIGN EDUCATION
Bibliographic record
Abstract
In the teaching of engineering design, it may be common to use design methodology (DM), as documented in several textbooks, in the course delivery. However, considerable drawbacks could be observed in our case when DM is taken as the major guidance for a capstone design course. We argue that DM tends to prescribe some context-free methods and procedures, which cannot be easily applied by students to their capstone design projects. At the same time, we observe that students need support to characterize a design problem, integrate technical knowledge in design activities and verify design ideas. These aspects require analytical and critical thinking, where DM may not be particularly helpful for students. In the five-year journey of deemphasizing DM in a capstone design course, we have explored and examined various pedagogical approaches such as online modules, design labs and peer evaluations. Without the teaching of DM, the pedagogical strategy needs to be carefully planned to deliver specific learning in engineering design.
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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 | 0.086 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| 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".