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Record W4396804517 · doi:10.46690/elder.2024.01.02

The effects of immersion in virtual reality environment on oral English learning for Chinese university students

2024· article· en· W4396804517 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

VenueEducation and lifelong development research. · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Educational Techniques
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsImmersion (mathematics)Virtual realityPsychologyMathematics educationMedical educationComputer scienceHuman–computer interactionMedicineMathematics

Abstract

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Most Chinese adults encounter difficulty in second language acquisition due to the lack of a second language environment to practice. The aim of this study was to examine the effects of immersive virtual reality learning environments on oral English learning for Chinese university students. A total of 43 freshmen participated in this study. Participants’ oral English performance, engagement, emotional and motivational status were compared between the fully immersive VR learning condition (using a head-mounted device) and the non-immersive condition (using a personal computer). Results indicated that participants in the fully immersive VR condition showed higher speaking accuracy, lower anxiety, and higher speaking-efficacy in the posttest than participants in the non-immersive VR condition. Participants in the fully immersive VR condition also showed more engagement than those in the non-immersive VR condition in all four experiment days. Theoretical and practical implications for applying VR technology in second language learning are discussed.Cite as: Li, J., Qiu, T., Li, C., Xu, C., Cheng, P.,Tang, Y., Georgiou, G. K. (2024). The effects ofimmersion in virtual reality environmenton oral English learning for Chineseuniversity students. Education andLifelong Development Research, 1(1), 3-14. https://doi.org/10.46690/elder.2024.01.02 References: Administering IPIP Measures, with a 50-item Sample Questionnaire. Retrieved October 18, 2021, from https://ipip.ori.org/New IPIP-50-item scale.htm#SampleQuestionnaire Altun, H. K., & Lee, J. (2020). Immersive Learning Technologies in English Language Teaching: A Meta-Analysis. International Journal of Contents, 16(3), 18–32. Bachman, L. F. (1990). Constructing measures and measuring constructs. In B. Harley, P. Allen, J. Cummins, & M. Swain ( Eds.), The development of second language proficiency ( pp. 26–49). Cambridge, England: Cambridge University Press. Bailey, J. 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The preliminary research on the relationship between arousal level and adult age differences of memory. Acta Psychologica Sinica, 26(2), 195–198. Yang, J. C., Chen, C. H., & Jeng, M. C. (2010). Integrating video-capture virtual reality technology into a physically interactive learning environment for English learning. Computers & Education, 55, 1346–1356. Yang, Z., Jia, W., Liu, G., & Sun, M. (2017). Quantifying mental arousal levels in daily living using additional heart rate. Biomedical Signal Processing & Control, 33, 368-378. Zeng, F. (2012). The reason and strategies of “dumb English”. Education Teaching Forum, 39, 71–72.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.421
Teacher spread0.384 · 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