Virtual Reality and Learning English as a Second Language with Young Learners: Impact of Classroom Culture
Bibliographic record
Abstract
This exploratory study investigated the impact of classroom culture on the use of high-immersive virtual reality (HVR) (Kaplan-Rakowski and Grubber, 2019) with young learners of English as a second language (ESL). Classroom culture was defined as the teacher’s views of her role in the classroom, group work and learner autonomy, the students’ use of the target language and learning strategies, and the use of technologies (Gagné and Parks, 2013). Participants consisted of 24 Grade 6 ESL students enrolled in an intensive program in the province of Quebec, Canada. Students worked in teams of four with two Meta Quest 2 head-mounted displays (HMD) as they carried out four communicative tasks in the software application Rec Room. Following a qualitative case study approach (Merriam and Tisdell, 2016), interviews were conducted with the teacher. Recordings of the students’ interactions and the researcher’s observations were also used to analyse the impact of classroom culture on how students engaged with the tasks. The analysis highlights the significance of classroom culture, revealing its connection to students' attitudes, behaviors, and strategies for managing the HMDs and communicative tasks. Pedagogical implications and suggestions for future research are also presented.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".