Online Learning as a Bolster for Sustainable Development in the Saudi EFL Context
Bibliographic record
Abstract
This study entirely depends on the premise that sustainable online education offers various benefits not only in the context of learning English as a foreign language but also in terms of a sustainable environment. The study’s central purpose is to explore the way through which online education contributes to sustainable development, which is in line with numerous sustainable objectives adopted by the Kingdom of Saudi Arabia to help achieve the 17 Sustainable Development Goals (SDGs), whose technical component is also acknowledged and accentuated by Saudi Arabia's 2030 Vision. In light of this, this study investigates the effects of online learning on sustainable growth in Saudi Arabia's post-COVID-19 higher education institutions, particularly in terms of quality education and clean climate. In doing so, the paper uses a mixed-method approach exemplified by quantitative and qualitative processes and manifested in two instruments: a questionnaire and an interview. The sample consists of 160 EFL students and 23 EFL teachers. All participants are affiliated with a Saudi university. Results reveal that online learning has significantly contributed to sustainable development, particularly in terms of quality education and clean climate. Concerning quality education, the results reveal a positive impact of online learning on enhancing both learners' autonomy and learners' willingness to communicate and decreasing learning anxiety. As for clean climate, results demonstrate that online learning has a significant, positive impact on environmental preservation and energy saving. Also, there was a strong and positive link between how well Saudi Arabia's higher education institutions handled the digital shift and their e-learning capabilities for sustainable growth, improvisational skills, and organizational preparedness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".