Understanding Sustainability Education Within Undergraduate Engineering at the University of Toronto
Bibliographic record
Abstract
Educating engineering students for sustainable development and environmental stewardship is an important preparatory foundation for addressing global complex problems. However, the necessary knowledge and learning for sustainability in engineering education may not be sufficient to meet these challenges. The research in this dissertation is investigating how engineers are being equipped to be sustainably responsible in their personal lives and professional practice. This research is multi-disciplinary including engineering, environmental, and education concepts. The undergraduate engineering students and curriculum at the University of Toronto (UofT) are featured as case studies. UofT is appropriate as the institution trains one of the largest cohorts of engineers in Canada, offering opportunities for engineering students to contribute positively to sustainability as future engineers in Canada. The three substantive studies represent the personal, professional and blended aspects of sustainability for undergraduate engineering students. In Study 1 – The Complex Relationship between Carbon Literacy and Pro-Environmental Actions among Engineering Students – I investigated students’ pro-environmental knowledge and actions pertaining to emissions and the relationship between them. I developed a survey and life cycle assessment-based methodology to estimate carbon footprint. The results indicated an overall weak relationship, but believing an action has high impact was associated with lower carbon footprint. In Study 2 – Crafting a Definition of Sustainability for Engineering Education and using it to Assess Curriculum – I developed a framework to describe sustainability in engineering and methodology to assess curriculum, then evaluated content in the undergraduate engineering curriculum at UofT by performing a qualitative analysis and surveying instructors. The results indicated that sustainability pillars tend to be taught in isolation rather than integrated. In Study 3 – Changes in Engineering Students’ Sustainability Perceptions and Engagement through Reflective Statements – I analyzed students’ personal statements from a sustainability related course. The results indicated a more balanced view of sustainability at the end of the course and development of broader societal thinking. Together these studies form a body of work that can be used to assess sustainability and be used to inform or recommend future plans in undergraduate engineering education programs. In addition, the instruments and generalizable methods can be extended to other institutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".