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PRONUNCIATION IN FACE-TO-FACE AND AUDIO-ONLY SYNCHRONOUS COMPUTER-MEDIATED LEARNER INTERACTIONS

Published online by Cambridge University Press:  10 February 2017

Shawn Loewen  and
Daniel R. Isbell
Shawn Loewen*
Affiliation:
Michigan State University
Daniel R. Isbell
Affiliation:
Michigan State University
*
*Correspondence concerning this article should be addressed to Shawn Loewen, Second Language Studies, B255 Wells Hall, 619 Red Cedar Road, Michigan State University, East Lansing, MI 48824-1027. E-mail: loewens@msu.edu
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Abstract

Studies of learner-learner interactions have reported varying degrees of pronunciation-focused discourse, ranging from 1% (Bowles, Toth, & Adams, 2014) to 40% (Bueno-Alastuey, 2013). Including first language (L1) background, modality, and task as variables, this study investigates the role of pronunciation in learner-learner interactions. Thirty English learners in same-L1 or different-L1 dyads were assigned to one of two modes (face-to-face or audio-only synchronous computer-mediated communication) and completed three tasks (picture differences, consensus, conversation). Interactions were coded for language-related episodes (LREs), with 14% focused on pronunciation. Segmental features comprised the majority of pronunciation LREs (90%). Pronunciation LREs were proportionally similar for same-L1 and different-L1 dyads, and communication modality yielded no difference in frequency of pronunciation focus. The consensus task, which included substantial linguistic input, yielded greater pronunciation focus, although the results did not achieve statistical significance. These results help clarify the role of pronunciation in learner-learner interactions and highlight the influence of task features.

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Footnotes

We would like to thank Dan Reed and Dennie Hoopingarner (Co-PIs) for funding the study’s data collection through their International Research and Studies Program Grant: Articulated Research: Web 2.0 Technology and Instructed Language Acquisition. In addition, we are indebted to Dominik Wolff and Xuehong (Stella) He for their assistance in data transcription and coding. Finally, we would like to thank the special issue editors for inviting us to be part of this special issue, as well as the anonymous reviewers who provided invaluable feedback.

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