Volume 27 Number 3, October 2023 Special Issue: Extended Reality (XR) in Language Learning Restructuring multimodal corrective feedback through Augmented Reality (AR)-enabled videoconferencing in L2 pronunciation teaching

Restructuring multimodal corrective feedback through Augmented Reality (AR)-enabled videoconferencing in L2 pronunciation teaching

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bitstream:
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collection:
Volume 27 Number 3, October 2023 Special Issue: Extended Reality (XR) in Language Learning
contributor.author:
Wen, Yiran Li, Jian Xu, Hongkang Hu, Hanwen
date.accessioned:
2023-09-29T22:41:37Z
date.available:
2023-09-29T22:41:37Z
date.issued:
2023-10-02
description.abstract:
The problem of cognitive overload is particularly pertinent in multimedia L2 classroom corrective feedback (CF), which involves rich communicative tools to help the class to notice the mismatch between the target input and learners’ pronunciation. Based on multimedia design principles, this study developed a new multimodal CF model through augmented reality (AR)-enabled videoconferencing to eliminate extraneous cognitive load and guide learners’ attention to the essential material. Using a quasi-experimental design, this study aims to examine the effectiveness of this new CF model in improving Chinese L2 students’ segmental production and identification of the targeted English consonants (dark /ɫ/, /ð/and /θ/), as well as their attitudes towards this application. Results indicated that the online multimodal CF environment equipped with AR annotation and filters played a significant role in improving the participants’ production of the target segments. However, this advantage was not found in the auditory identification tests compared to the offline CF multimedia class. In addition, the learners reported that the new CF model helped to direct their attention to the articulatory gestures of the student being corrected, and enhance the class efficiency. Implications for computer-assisted pronunciation training and the construction of online/offline multimedia learning environments are also discussed.
endingpage:
107
entity.type:
None
identifier.citation:
Wen, Y., Li, J., Xu, H., & Hu, H. (2023). Restructuring multimodal corrective feedback through Augmented Reality (AR)-enabled videoconferencing in L2 pronunciation teaching. Language Learning & Technology, 27(3), 83–107. https://hdl.handle.net/10125/73533
identifier.issn:
1094-3501
identifier.uri:
https://hdl.handle.net/10125/73533
number:
3
publicationname:
Language Learning & Technology
publisher:
University of Hawaii National Foreign Language Resource Center Center for Language & Technology
rights:
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
rights.uri:
https://creativecommons.org/licenses/by-nc-nd/4.0/
site_url:
/item/10125-73533/
startingpage:
83
subject:
Corrective Feedback Multimedia Learning Computer-assisted Pronunciation Training (CAPT) AR-enabled Videoconferencing
title:
Restructuring multimodal corrective feedback through Augmented Reality (AR)-enabled videoconferencing in L2 pronunciation teaching
type:
Article
volume:
27