Developing skills for sustainable development through community-focused learning
Bibliographic record
Abstract
For engineering students to become leaders in sustainable development, they need to develop the ability to apply empathy in partnership with communities and collaborate across disciplinary boundaries. This is difficult to facilitate in the dominant engineering education paradigm that emphasizes technical ability while social engagement is not stressed as much. We have implemented community-focused experiential education to foster skills for leadership in sustainable development. Experiential education is based on the theory of situated learning, in which students learn through the praxis of authentic environments. Implementing experiential learning in the classroom exposes engineering students to the necessary skills to deal with wicked and complex problems. It creates an environment that gives space for failure and learning then iteration. The implementation of experiential learning in the course reimagines how engineering education can be structured with an emphasis on community engagement and creative problem-solving. In this paper, we introduce an innovative community-based learning (CBL) integrated course with multiple community partners and stakeholders. Students in the course had the opportunity to interact with faculty, industry, and policymakers outside their discipline.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".