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Record W4409852522 · doi:10.1080/09588221.2025.2497494

Immersive virtual reality and language learning: activity theory perspectives

2025· article· en· W4409852522 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComputer Assisted Language Learning · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversité LavalUniversité TÉLUQ
Fundersnot available
KeywordsComputer scienceLanguage acquisitionTeaching methodVirtual realityLinguisticsHuman–computer interactionMultimediaCognitive scienceMathematics educationPsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.272
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it