Exploring Attitudes towards Embedding Education for Sustainable Development in Curriculum Design
Bibliographic record
Abstract
This paper shares insights from the research conducted during the 2022-2023 Learning Design and Education for Sustainable Development Bootcamp. The Bootcamp was designed by the Association for Learning Design and Education for Sustainable Development (ADLESD) and delivered in collaboration with UNESCO International Institute for Higher Education in Latin America and the Caribbean (IESALC). The Bootcamp is supported by the CoDesignS ESD Framework and has been executed in collaboration with several prestigious institutions. The study aimed to identify any changes in attitudes towards embedding Education for Sustainable Development (ESD) into curriculum design following the Bootcamp. Using a validated survey that measured four attitude components - affective, perceived control, usefulness, and behavioural - significant variations were observed in the latter three components post-Bootcamp. This shows an increased perception of ESD's practical value, participant confidence in implementing it, and readiness to adjust behaviours to accommodate ESD principles. However, the affective component, or emotional response to ESD, remained largely unchanged, likely due to the Bootcamp participants' self-selection. Particularly notable was the boost in the perceived control element, possibly due to the clear pedagogical framework and toolkit provided during the Bootcamp. This improvement in the Control component and the overall positive impact of the Bootcamp are consistent with feedback obtained from participants in the final evaluation survey. These findings indicate that the Bootcamp and the use of the CoDesignS ESD Framework and Toolkit Planner significantly increased participants' willingness, confidence, and ability to integrate ESD into curriculum design effectively.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".