Developing an ESP Syllabus to Promote Sustainable Development Goals: A Delphi Study
Bibliographic record
Abstract
The syllabus design for ESP should account for specific learners’ and industry needs regarding language skills, aspects and functions. Furthermore, the process should consider the globally trending challenges to prepare learners for their prospective jobs. In this regard, UN Sustainable Development Goals (SDGs) are comprehensive agenda that shapes the world's future in different interconnected domains. Thus, incorporating them in ESP syllabi can promote knowledge about Sustainable Development (SD) as suggested by UN SDG 4 on Quality Education and contribute to the achievement of other goals, such as UN SDG 8 on Decent Work and Economic Growth. Subsequently, the present study adopts the Delphi technique to explore 23 multinational and multidisciplinary experts on the ways to develop a needs-analysis-based tourism ESP syllabus that promotes the UN SDGs and enables learners to effectively communicate them in their future careers. The results show that although the current syllabus is aligned with target learners' needs and the specified goals, it lacks appropriate alignment with UN SDGs and sustainable tourism. The experts recommend updating learning goals and outcomes to align the syllabus with the UN SDGS and enrich it with more SD-related content. The study's implications highlight the need for further research on adapting ESP syllabi across disciplines to address globally trending issues and initiatives outlined in the UN SDG framework.
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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 | 0.039 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".