Integrating UN SDGs in ELT: How Far Tertiary EFL Teachers in Oman Are Successful
Bibliographic record
Abstract
Sustainability has emerged as a global priority, promoting educators across disciplines to integrate sustainability-related themes into their curricula. In English Language Teaching (ELT), embedding the United Nations’ Sustainable Development Goals (SDGs) offers an opportunity not only to enhance linguistic competence but also to foster global citizenship, critical thinking, and socio-cultural awareness. This study investigated how tertiary-level English as Foreign Language (EFL) teachers in Oman perceive and integrate SDGs into their ELT practices. The study also explored the challenges encountered, as well as the support needed to ensure successful and meaningful fusion of the SDGs into ELT practices. Adopting a mixed-methods approach in an explanatory, sequential design, the study involved 82 EFL teachers from private universities across Oman. Data were collected through a structured questionnaire and focus group discussions. The findings indicated that while the majority of the participants acknowledged the value of embedding SDGs in ELT; particularly for enhancing students’ critical thinking and global awareness, most were still in the early stages of the comprehensive integration. However, several significant barriers were identified, including students’ low language proficiency, limited instructional time, lack of appropriate teaching resources, unclear assessment practices, instructional overload, and insufficient institutional support. The study concluded that there is a need for targeted professional development, institutional collaboration, and support systems to enhance the integration of SDGs within the ELT context in Oman. For EFL teachers, they can project role models for learners in sustainability-oriented education, along with themes like energy saving and maintaining class equipment, to raise students’ awareness.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".