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A framework for learner agency in online spoken interaction tasks
Published online by Cambridge University Press: 22 May 2017
Abstract
Learner agency, the capability of individual human beings to make choices and act on these choices in a way that makes a difference in their lives (Martin, 2004), is instrumental in second language learning because attainment is only arrived at by learner choice (Pavlenko & Lantolf, 2000). If attainment is understood as learner engagement in synchronous, collaborative, spoken interaction which is thought to lead to gains in second language acquisition (SLA), then design considerations that harness learners’ agency towards that end is important. This study explores the relationship between learner agency and two different task types, namely an information-gap task and an opinion-sharing task in two peer-to-peer synchronous computer-mediated communication (SCMC) spoken interaction events. Students’ choices and how students act on these choices during tasks are analysed using a discourse analysis approach. Audio recordings of four dyads as cases were examined using three analytical dimensions: language functions of verbal interaction, cognitive processing and social processing. The results show that most learners used their agency to reconfigure the tasks from spontaneous to planned interaction, with some choices and actions relating to technology impacting detrimentally on interaction time in the target language. The different tasks were found to filter and channel different types of agency that learners could exercise, namely representational, organisational, and strategic agency as speech acts, and directional agency as a physical act. These types consisted of different natures and purposes and are presented as a framework. The information-gap task supported strategic agency and an opinion-sharing task supported personalisation and identity construction or representational agency.
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- Regular papers
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- Copyright
- Copyright © European Association for Computer Assisted Language Learning 2017
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