COMMUNITY FINDING AS LEARNING: IMPACTS OF A NOVEL MODEL FOR COMMUNITY ENGAGED, PROJECT BASED LEARNING IN ENGINEERING
Bibliographic record
Abstract
This paper explores the impact of a specific implementation of community engaged engineering design pedagogy. By asking students about their experience in choosing, engaging with, and researching a community to develop an understanding of and clearly articulating a design problem in a Requests for Proposals, we seek to understand how student initiated community engaged learning (CEL) can contribute to learning about design. Results of the survey show that students did pick up skills and experiences that reflected course and activity learning objectives. Students engaged and relied on – sometimes to their detriment – personal contact and communication with stakeholder communities for information. They expressed an awareness of the importance of personal investment in the community, even if that investment was limited in their own projects. Not unexpectedly, students also reported a preference for and greater perceived learning in a more conventional design education experience. However, the act of community finding and engagement did impact their understanding of engineering design, particularly around often neglected aspects, and helped them to see design more holistically.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".