Promoting Intercultural Competence in a Learning Activity Supported by Virtual Reality Technology
Bibliographic record
Abstract
Virtual reality (VR) technology makes it possible to create an authentic virtual environment that benefits immersive learning. We designed an intercultural learning activity and applied VR technology to support it. Then, we investigated students’ perceptions of the learning activity, VR technology, and intercultural competence (IC) development during learning. Students from China and Uzbekistan participated in the activity, in which a pragmatic mixed-methods approach was used. The data were collected through student reports, three questionnaires, and interviews, and then analyzed. Three main findings were obtained. First, 13 items related to perception of the learning activity were revealed. When compared with earlier studies, new items were found, including presence, immersion, and authentic cultural experience. Second, the results showed that the participants intended to continue using VR technology, were satisfied with intercultural learning supported by VR technology, and felt that the technology confirmed their expectations. Third, the results showed that intercultural learning supported by VR technology helped facilitate IC development. Based on these results, we discuss implications and offer suggestions for educators and researchers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 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".