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Examining focused L2 practice: From in vitro to in vivo

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Title: Examining focused L2 practice: From in vitro to in vivo
Authors: Cornillie, Frederik
Van Den Noortgate, Wim
Van den Branden, Kris
Desmet, Piet
Keywords: Behaviour-tracking Technology
Computer-assisted Language Learning
Second Language Acquisition
Grammar
Syntax
Issue Date: 01 Feb 2017
Publisher: University of Hawaii National Foreign Language Resource Center
Michigan State University Center for Language Education and Research
Citation: Cornillie, F., Van Den Noortgate, W., Van den Branden, K., & Desmet, P. (2017). Examining focused L2 practice: From in vitro to in vivo. Language Learning & Technology, 21(1), 121–145. https://dx.doi.org/10125/44598
Abstract: Behaviour-tracking technology has been used for decades in SLA research on focused practice with an eye toward elucidating the nature of L2 automatization (e.g. DeKeyser, 1997; Robinson, 1997). This involves longitudinally capturing learners’ judgments or linguistic production along with their response times in order to investigate how specific skills become automatic over time. However, previous research in this area has been conducted mostly in laboratories (i.e., in vitro), sometimes with artificial languages, thereby compromising ecological validity of the findings. Building on this work, this article reports on a one-month study in which learners’ (N = 126) behaviour was tracked while they practised two constructions of English grammar (varying in complexity) using mini-games that involved some time pressure and were embedded in meaning-focused reading and discussion activities in class. Feedback was randomly varied between participants. Multilevel statistical analyses of accuracy and response time suggest that practice helped to develop automaticity, and that rule complexity and metalinguistic feedback played a role. The methodological innovation of this study consists of the application of in vitro experimental research techniques in in vivo L2 learning contexts and of the use of statistical mixed effects models to account for the complexity of real-life tracking data.
URI/DOI: http://hdl.handle.net/10125/44598
ISSN: 1094-3501
Appears in Collections:Volume 21 Number 1, February 2017 Special Issue on Methodological Innovation in CALL Research


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