An exploration of low- and high-immersive virtual reality modalities for willingness to communicate in English as a second language
Bibliographic record
Abstract
This mixed-method quasi-experimental study compares the impact of two different kinds of virtual reality environments on the willingness of English as a Second Language (ESL) students to engage in communication. Twenty high-level ESL graduate students were recruited for the research and organized into 10 pairs. Each pair of students completed two separate speaking tasks. One task took place in a low-immersive virtual reality environment, while the other task took place in a high-immersive virtual reality environment. The study was counterbalanced, with half completing the two tasks in one order, while the other half completed them in the reverse order. The study found no statistically significant differences between the modality conditions on willingness to communicate. Task order and action-oriented instructional methods were found to have greater impact than the modality. However, a significant difference was found between participants' affective-cognitive experiences, with participants reporting higher cognitive load and greater enjoyment in high-immersive condition. Speaking anxiety was also reduced after participation in two virtual reality tasks, leading to an increase in self-confidence. Additionally, qualitative analysis identified relationships between various technological, affective-cognitive, and individual factors that can affect the student's willingness to communicate. Empirical and theoretical implications are discussed, along with limitations and directions for future research.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".