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Effects of Technology-Enhanced Language Learning on Reducing EFL Learners' Public Speaking Anxiety

Authors :
Yi-chen Chen
Source :
Computer Assisted Language Learning. 2024 37(4):789-813.
Publication Year :
2024

Abstract

Public speaking is considered the most anxiety-provoking speaking activity for English as a foreign language (EFL) learner. While traditional lecture-based classrooms hinder EFL learners' constant practice and frequent interaction due to large class sizes and limited time, recent developments in technology, including Artificial Intelligence (AI), Automatic Speech Analysis, and Virtual Reality (VR), may enhance language learning by offering accessible and personalized learning experiences. This study aimed to investigate the effects of technology-enhanced learning on reducing EFL learners' PSA. Thirty-three university students were divided into three groups and received either lecture-based, mobile-assisted, or VR-facilitated instruction for four weeks. The students' perceived PSA levels were reduced in all three groups after their respective instruction, but only the VR-facilitated group reached statistical significance, and there were no differences in the three groups' final oral performances. However, the two technology-enhanced language learning (TELL) groups achieved more convergent performances on the Personal Report of Public Speaking Anxiety scale compared with the non-TELL group, which indicated that the impact of individual differences may have been compensated by technology assistance. The findings suggested that the instructional feedback generated by AI decreased the participants' PSA, although the irreplaceable role of teachers as facilitators was also emphasized, while the potential of using VR in teaching public speaking was evident.

Details

Language :
English
ISSN :
0958-8221 and 1744-3210
Volume :
37
Issue :
4
Database :
ERIC
Journal :
Computer Assisted Language Learning
Publication Type :
Academic Journal
Accession number :
EJ1428207
Document Type :
Journal Articles<br />Reports - Research
Full Text :
https://doi.org/10.1080/09588221.2022.2055083