The impact of multidisciplinary program requirements on student attitudes toward sustainability and education for sustainability
Bibliographic record
Abstract
Purpose This paper aims to investigate the role of multidisciplinary course requirements in shaping student attitudes toward sustainability and education for sustainable development. Previous research indicates that students conceptualize sustainability based on their academic discipline; thus, this research investigates whether there is a difference in student attitudes toward sustainability at Grenfell Campus, Memorial University of Newfoundland, where students are encouraged to cross the borders of their academic disciplines. Design/methodology/approach This research reports on the findings from a mixed-methods study to assess the impact of program requirements on student attitudes toward sustainability and education for sustainable development at Grenfell Campus, Memorial University of Newfoundland. In phase one, an anonymous survey was administered to students regarding their perceptions and attitudes toward sustainability and education for sustainable development. The survey yielded 100 usable responses. Phase 2 consisted of a series of 10 semi-structured expert interviews with key faculty and staff at Grenfell Campus and a representative from the City of Corner Brook, which gave further insights regarding sustainability programming and campus culture. Findings Contrary to previous research, the results of this research indicate that the School of Study does not have a statistically significant impact on student attitudes toward sustainability. This may be attributed to Grenfell Campus’s Breadth of Knowledge requirement within the School of Arts and Social Science and the School of Science and the Environment, which requires that students take elective courses from a broad range of subject matter to develop their holistic awareness of social, cultural, scientific and political issues. Practical implications The results of this research indicate that students who are exposed to broad multidisciplinary requirements may be more likely to have positive attitudes toward sustainability than students who focus on a single discipline. Originality/value A limited number of studies investigate the impact of core program requirements on student attitudes toward sustainability. This paper promotes an effective way of raising sustainability-literate young people/citizens in a Canadian higher education context.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".