Stickler, Ursula and Shi, Lijing 
ORCID: 0000-0002-0436-5758
(2017)
Eyetracking methodology in SCMC: a tool for empowering learning and teaching.
ReCALL, 29 (2).
pp. 160-177.
ISSN 0958-3440
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Abstract
Computer-assisted language learning, or CALL, is an interdisciplinary area of research, positioned between science and social science, computing and education, linguistics and applied linguistics. This paper argues that by appropriating methods originating in some areas of CALL-related research, for example human-computer interaction (HCI) or psycholinguistics, the agenda of “attention-focus” research can be shifted from a cognitive perspective to a learner-centred approach; understanding online language learning and teaching spaces as mediated by technology; second/foreign language learning; and online teaching culture. Taking a method that has traditionally been used within a positivist paradigm, the authors exemplify the potential of eyetracking to advance online language learning research – extending it in ways compatible with a sociocultural paradigm. This is evidenced by two pioneering studies in which an innovative combination of methods allows participants, whose gaze focus was recorded during synchronous computer-mediated communication (SCMC), to reflect back on their involvement. Eyetracking is combined with stimulated recall interviews that trigger deep reflection on learner and teacher strategies by directing participants’ recollections on their attention focus. The rich, multifaceted results shown by this original and innovative use of eyetracking methods in a sociocultural framework suggest a way forward in researching online learning by integrating insider and outside views coherently and systematically.
| Item Type: | Article |
|---|---|
| Official URL: | https://www.cambridge.org/core/journals/recall |
| Additional Information: | © 2017 European Association for Computer Assisted Language Learning |
| Subjects: | L Education > LB Theory and practice of education Q Science > QA Mathematics > QA76 Computer software |
| Date Deposited: | 14 Mar 2017 17:03 |
| Last Modified: | 21 Nov 2024 07:21 |
| URI: | http://eprints.lse.ac.uk/id/eprint/69815 |
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