L2 learners’ engagement with automated feedback: An eye-tracking study

Liu, Sha
Yu, Guoxing
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University of Hawaii National Foreign Language Resource Center
Center for Language & Technology
(co-sponsored by Center for Open Educational Resources and Language Learning, University of Texas at Austin)
This study used eye-tracking, in combination with stimulated recalls and reflective journals, to investigate L2 learners’ engagement with automated feedback and the impact of feedback explicitness and accuracy on their engagement. Twenty-four Chinese EFL learners revised their writing through Write & Improve with Cambridge, a new automated writing evaluation system that generates automated feedback with three different levels of explicitness. Data from multiple perspectives were collected and examined, including participants’ eye movements, their stimulated recalls, and their responses/revisions to automated feedback on their multiple drafts. The results revealed that participants spent significantly more time and expended more cognitive effort in processing indirect than direct feedback. However, a lower percentage of indirect feedback was taken up, and the revisions participants made based on such feedback were less successful. These findings suggest feedback explicitness as a determining factor affecting learners’ engagement with automated feedback and point to the need for timely, supplemental teacher or peer scaffolding in addition to automated feedback. The results also suggest that AWE tools need to be constantly updated to improve their feedback accuracy, as error-prone feedback may cause participants to make inaccurate amendments to their writing. In addition, teachers should help learners confirm the accuracy of AWE feedback.
Explicitness of Automated Feedback, Accuracy of Automated Feedback, L2 Learner Engagement, Eye-tracking
Liu, S., & Yu, G. (2022). L2 learners’ engagement with automated feedback: An eye-tracking study. Language Learning & Technology, 26(2), 78–105. https://doi.org/10125/73480
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