Working with “Others”: Developing Sustainability Skills in the First Year Engineering Classroom
Bibliographic record
Abstract
Abstract While engineering education excels at training students to solve well-defined and highly structured problems, it struggles to support the development of students’ abilities to address highly complex, ill-structured, and contested engineering problems that lack in definite solutions, where engineers are called on to work with non-engineers in a transdisciplinary environment. The challenge for engineering educators is to develop and teach constructively aligned curricula aimed at developing transdisciplinary skills so that, as practitioners, graduating engineering students contribute to addressing these types of problems within transdisciplinary environments.
 Efforts are underway in many institutions to close the gap between the transdisciplinary needs in practice and current engineering curricula. At the University of British Columbia (UBC), a team of faculty members and engineering practitioners have recently developed and are teaching a design-focused engineering course to all first year students. In this paper we, a subset of UBC’s teaching team, present the argument for teaching skills to engineering students that support transdisciplinary. Wesummarize the definitions of these skills found in the literature, and we speculate that the development of one aspect of transdisciplinary is related to personal development. Specifically, we hypothesize that systems thinking is correlated to metacognition. We describe an experimental strategy for testing the hypothesis within a first year engineering program, then we present and discuss preliminary test results.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".