Video chat vs. face-to-face recasts, learners' interpretations and L2 development: a case of Persian EFL learners. Issue 1 (17th February 2017)

• Record Type: Journal Article
• Authors: Rassaei, Ehsan
• Is Part Of: Computer assisted language learning. Volume 30:Issue 1/2(2017)
• Page Start: 133
• Page End: 148
• Publication Date: 2017-02-17
• Subjects: Recasts -- corrective feedback -- video chat -- computer-mediated feedback; Language and languages -- Computer-assisted instruction -- Periodicals; 418.00285
• DOI: 10.1080/09588221.2016.1275702
• Languages: English
• ISSNs: 0958-8221

Abstract

ABSTRACT: This study investigated the effects of two modes of corrective feedback, namely, face-to-face recasts and computer-mediated recasts during video-conferencing on Iranian English as a foreign language (EFL) learners' second language (L2) development. Moreover, the accuracy of the learners' interpretations of recasts in the two modalities was examined. Fifty-seven EFL learners were assigned into three conditions. In the first condition, learners were involved in dyadic face-to-face communication with their teacher during which learners' errors were corrected by the teacher via recasts. In the second treatment condition, learners communicated with their teacher through video chat (using Skype) in a laboratory and received oral computer-mediated recasts for the errors they made. Finally, the learners of the control condition were involved in face-to-face communication but received no corrective feedback for their errors. Learners' development was measured through an oral production task and an error correction test. The results indicated that computer-mediated and face-to-face recasts were equally effective for enhancing learners' L2 development. Furthermore, analyzing learners' perceptions of recasts elicited through stimulated recall interviews revealed no statistical difference in learners' interpretations of recasts in the two instructional modes. The study concludes that video-based online oral corrective feedback can be as effective as traditional face-to-face feedback.