Bibliographic record
Abstract
Abstract We aim to examine the potential of using analogies in design education and to compare the roles of analogies in explaining versus inspiring in engineering design. We review existing research in analogical thinking, with a focus on scientific discourse and education. Then we explore the role of analogies in design education in making concepts more relatable by asking six participants in a graduate-level design course to generate analogies for course topics. We describe criteria developed to evaluate the analogies and present these evaluations. We then asked participants to perform divergent thinking tests, but we found no significant correlation between these and analogy scores. The participants were also asked to reflect on what constitutes an effective analog, describe their process of identifying analogies, and provide their definitions of analogies. We describe possible links between these comments and the ratings of their analogies. We then draw on results in using analogies in pedagogy to inform and reflect on obstacles we encountered in the use of analogies to inspire. Specifically, we related them to our experience with biomimetic or biologically inspired design, where we used a natural-language search approach to identify relevant analogies. Three aspects discussed are familiarity of source analogies, boundaries of parallels between source analogies and target concepts, and concreteness of source analogies. Finally, we discuss possible pedagogical benefits of eliciting analogies on course topics from students, namely, using the elicited analogies as tools for improved student engagement as well as more prompt instructor feedback.
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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 | 0.001 |
| 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".