ADVANCING ENGINEERING EDUCATION TOWARDS DESIGN FOR SUSTAINABLE DEVELOPMENT AT THE UNIVERSITY OF MANITOBA: INITIAL ASSESSMENT
Bibliographic record
Abstract
This study is part of an on-going project designed by the NSERC Chair in Design Engineering for Sustainable Development and Enhanced Design Integration at the Price Faculty of Engineering, University of Manitoba. In this paper, findings on how educators are advancing engineering education towards design for sustainable development at the University of Manitoba is presented. A faculty-wide-survey and a follow-up one-on-one conversation were used to gather information from educators on incorporating sustainable development design fundamentals, which were framed around the United Nations Sustainable Development Goals (UN-SDGs) in their undergraduate courses. The findings showed that the level of engagement with the current call to shift from the existing “traditional engineering systems” to one of sustainability is still in early stages. Educators who are actively engaging with the subject matter indicated that the level of difficulty of incorporating sustainable development design fundamentals into their courses depends on the nature of the course. Courses that are designed to address environmental and/or societal issues were perceived by educators as a natural fit. For technical courses, educators were intentional about integrating sustainable development design fundamentals. Yet, educators expressed difficulties due to lack of resources addressing sustainable development concepts relevant to the specific subject areas or lack of means to assess students’ work with respect to social equity and environmental stewardship. Consequently, there is a natural tendency towards the economic viability of designs with less emphasis on social equity and environmental stewardship. Also, educators suggested the need for capacity building to help better understand this paradigm shift and equip them to advance engineering education towards sustainable development. Based on the findings, the Chair organized a capacity-building workshop for educators on “Technological Stewardship” facilitated by the Engineering Change Lab to assess the opportunities of the engineering community and view their practice through this lens. The authors of this paper participated in the workshop and they have shared their gained perspectives on the subject.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".