Immersive virtual reality and language learning: activity theory perspectives
Bibliographic record
Abstract
The present study investigated the use of immersive virtual reality (IVR) for language learning with young learners of English as a second language (ESL). Drawing on activity theory, it aimed to understand the engagement of teams of four students during four communicative tasks with the Meta Quest 2 head-mounted display. Recordings, questionnaires and observations were used to carry out an activity systems analysis and to identify contradictions as they arose during tasks. In addition, one high-functioning and one low-functioning team were selected to take part in interviews which aimed to understand how they engaged differently. The analysis revealed that students experienced 14 different tensions which were mainly related to classroom culture, virtual reality, and the language learning tasks. The comparisons between the high-functioning and low-functioning teams revealed that they experienced different tensions related to different factors such as their view of language learning. The present study shed light on student behavior and engagement during IVR use for language learning as part of communicative tasks with young learners.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".