Voice-user interfaces for TESOL: Potential and receptiveness among native and non-native English speaking instructors
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University of Hawaii National Foreign Language Resource Center
Center for Language & Technology
(co-sponsored by Center for Open Educational Resources and Language Learning, University of Texas at Austin)
Center for Language & Technology
(co-sponsored by Center for Open Educational Resources and Language Learning, University of Texas at Austin)
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25
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3
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27
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39
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Abstract
Initial research, although limited, demonstrates promise for the use of a voice-user interface via a digital assistant (i.e., Google Assistant) for English language learners seeking language skill development. However, no research has sought to determine the adult English as a foreign language instructor response toward the application of such devices. This study addresses that gap by seeking to determine the pedagogical value of such an interactional modality by native-English speaking (NES) and non-native English speaking (NNES) instructors (n=12) enrolled in an MA(TESOL) program in Korea. Particular focus centers on the potential regarding the deployment of such a device with learners by these instructors, and their receptiveness toward using such a device from within their educational contexts. An exploratory qualitative method employing a semi-structured interview technique was undertaken. A concept driven coding approach in data analysis was then employed to develop a framework of pedagogical prospects regarding digital assistant use, built on aspects emerging from the concepts of comfortability, comprehension, usability, enjoyability, and worthwhileness. Results highlight that instructor perceptions regarding the potential use of voice-user interfaces in the classroom tend to align, although some minor differences did emerge, and that all are receptive to its use in a multitude of ways.
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Kent, D. (2021). Voice-user interfaces for TESOL: Potential and receptiveness among native and non-native English speaking instructors. Language Learning & Technology, 25(3), 27–39. https://doi.org/10.64152/10125/73444
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