Education for Sustainable Development (ESD) Infusion into Curricula: Influences on Students’ Understandings of Sustainable Development and ESD
Bibliographic record
Abstract
Formal education for sustainable development (ESD) is in large part dependent on capacity-building and training of teachers as they are the individuals who must both deliver ESD at the classroom level as well as utilise their own knowledge, values and skills in support of sustainability. In this research, teacher educators within a higher education institution in Jamaica undertook a collaborative action research project to infuse ESD into their selected undergraduate and postgraduate courses during the spring semester of the 2018/19 academic year. Data were collected from approximately 140 students through the use of a pre- and post-infusion concept map, which sought to ascertain various facets including students’ level of awareness and perspectives on sustainable development and ESD. Preliminary findings indicate that students’ understanding of sustainable development broadened after the courses, with most students believing that sustainable development involves social, economic and environmental improvements that do not come at the expense of our natural resources. Additionally, students’ thoughts about ESD shifted, with students highlighting aspects of the interdisciplinary nature of ESD and ESD as involving equitable and inclusive education, as well as attitudinal and behavioural changes. The findings of this research are significant in highlighting how the intentional infusion of ESD into courses across various specialisations can enhance students’ knowledge and awareness of sustainable development and ESD.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".