CLASSIFICATION OF GENERIC DESIGN TASKS TO PROMOTE DESIGNER FLEXIBILITY AND INTEGRATION SKILLS IN CAPSTONE PROJECTS
Bibliographic record
Abstract
Designer flexibility is referred to as an ability to adopt design tools and engineering knowledge to solve design problems. As design methodology is intended to be general for different kinds of design problems, it would not be particularly helpful for designers to connect technical content to specific design applications, and students often face challenges with this connection. To address this issue,we propose five types of generic design tasks, which are applied as a platform for students to integrate their knowledge and skills for design work. These generic design tasks are background research, problem framing, idea generation, decision making and scientific analysis, which can take place in multiple design stages. After mapping design tasks and stages, we can provide commonvocabulary for multidisciplinary design, define skill levels for design assessments, suggest a “reverse” learning path to train design skills from well-defined to open-endedproblems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".