Exploring English as a Foreign Language Japanese Learners’ Perceptions of Virtual Reality-Based Speaking Practice
Bibliographic record
Abstract
Virtual reality (VR) offers unique advantages in English as a foreign language (EFL) learning by providing immersive contexts that are difficult to replicate in traditional classrooms. In this practice-based study we examine the integration of VR into task-based speaking activities through virtual tours of learners’ preferred locations in Japan. Sixteen Japanese university students participated in VR-based speaking tasks and completed a post-activity survey. Learners generally reported that the authentic VR environment encouraged language output, reduced anxiety, and enhanced motivation, though they also noted challenges such as technical difficulties, vocabulary limitations, and occasional physical discomfort. A tentative comparison by Test of English for International Communication (TOEIC) median split suggested differing tendencies: students with lower scores (< 575 TOEIC score) highlighted enjoyment and perceived progress, whereas those with higher scores (≥ 575 TOEIC score) more often noted task-related challenges. These patterns should be interpreted cautiously given the small sample size and the preliminary, exploratory basis of the grouping. Even so, the findings contribute to the field by identifying VR’s potential as a complementary pedagogical tool and by outlining a research agenda for larger and longer studies that can more fully determine its role in language education.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".