Teaching Biomimicry With an Engineering-to-Biology Thesaurus
Bibliographic record
Abstract
This paper presents research on the use of an engineering-to-biology thesaurus in an engineering classroom as an aid to teaching biomimicry. The leap from engineering to biological science has posed a challenge. Engineers often struggle with how to best use the vast amount of biological information available from the natural world around them. Often there is a knowledge gap, and terminology takes different meanings. Generally, the time required to learn and become fluent in biology poses too large a hurdle. The engineering-to-biology thesaurus was designed to allow engineers without advanced biological knowledge to leverage nature’s ingenuity during engineering design. The three key goals of this thesaurus are to (1) lessen the burden when working with knowledge from the biological domain by providing a link between engineering and biological terminology; (2) assist designers with establishing connections between the two domains; and (3) to facilitate biologically-inspired design. In this paper, the results of a pilot study as well as a second study are presented. The pilot study was used to craft instructional materials involving the engineering-to-biology thesaurus. In the second study, sophomore engineering students enrolled in a design course were given a design task to complete using the thesaurus. The task focused on biomimetic concept development for their course project — designing a human-powered vehicle for a person with cerebral palsy. Results of the design task are presented.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".