Virtual Reality in TEFL Context, Instructors’ Perspectives in a Saudi University
Bibliographic record
Abstract
The purpose of this study is to explore instructors’ perspectives in a Saudi university about using virtual reality in TEFL context. The sample of the study consisted of 6 instructors randomly selected from different faculties affiliated to Al-Baha University. The instrument of the study is based on semi-structured interviews administered to the targeted participants. The researcher used SWAT analysis to process the elicited data. The Findings of the study indicate that strength points of using virtual reality to teach English are VR is exciting, authentic, and more interactive learning style for English language learners as compared to conventional learning style, weakness points are financial setbacks of implementing VR and the inexperienced instructors who need training to implement VR to teach English. The results indicate that there is only one main threat of using VR to teach the English language in the Saudi context, namely that VR could be a distraction for some students. This study generates new insights into processes of adopting VR to teach English language in the Saudi context and the potential strength, weakness, opportunity, and threats to such adoption in the target university. The study concluded with recommendations to the concerned institutions for the betterment of using VR in EFL contexts. The researcher suggests further studies to be conducted in similar contexts for using VR in EFL institutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".