Exploration of Skills Needed for Graduate-Level Community Engaged Learning in Engineering
Bibliographic record
Abstract
Community-engaged learning in graduate engineering programs has had growing attention in engineering education research to prepare researchers for supporting communities in solving complex problems. As a practice, community-engaged learning in engineering struggles to achieve equitable outcomes for community partners because students in such programs may lack skills or possess charity or patriarchal mindsets. A charity mindset is characterized by an uncritical desire to help, with Western engineering deemed favourable over community knowledge systems resulting in the design of solutions that try to address symptoms of inequity without meaningful community involvement. With the increase of community-driven and engaged learning in engineering graduate studies, pushed by research agencies and the marketing of large-scaled international grand challenges, the development of engagement skills in graduation education is needed. In this paper, we highlight the specific successes and challenges faced by one graduate student and their supervisors undertaking community-engaged learning. We explore these issues while embedded in a project with three Indigenous communities on improving resilience in an engineering procurement and construction (EPC) process for healthcare facilities in Indigenous communities by incorporating cultural and social factors. We bring forward these successes and challenges to build a conversation around designing curricula for engineering-engaged learning skills for graduate students.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".