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Mature ELLs’ Perceptions Towards Automated and Peer Writing Feedback

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11722))

Abstract

Mature English Language Learners (ELLs) learning to write in informal environments have little access to instructor feedback and must rely on other sources to support their writing development. While it is known that mature ELLs trust instructor feedback, their perceptions towards feedback from non-expert sources may be mixed. We report on mature ELLs’ perceptions and interpretations of peer and automated feedback when using dashboard visualizations of their writing skills derived from several metrics and sources of feedback. These perceptions and interpretations were collected through a short-term deployment of the dashboard within a writing app with 16 mature ELLs, followed by interviews with the learners. From analyses of these interviews, we suggest three design guidelines (DG) related to learning analytics dashboard design for mature ELLs in informal learning contexts. First, analytics-based feedback should contextualize ELLs’ learning progress by providing temporal information about learner performance. Second, justifications should accompany feedback to avoid criticism arising from ELLs’ prior beliefs. Third, learner autonomy should be fostered by offering explicit mechanisms for reflecting on feedback that is inconsistent with learner beliefs since learners are willing to question automated feedback. We discuss how these three guidelines can be used to benefit learners when an instructor is not present.

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Acknowledgements

This work was supported by AGE-WELL NCE Inc., a member of the Government of Canada’s Networks of Centres of Excellence.

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Correspondence to Amna Liaqat .

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Liaqat, A., Akcayir, G., Demmans Epp, C., Munteanu, C. (2019). Mature ELLs’ Perceptions Towards Automated and Peer Writing Feedback. In: Scheffel, M., Broisin, J., Pammer-Schindler, V., Ioannou, A., Schneider, J. (eds) Transforming Learning with Meaningful Technologies. EC-TEL 2019. Lecture Notes in Computer Science(), vol 11722. Springer, Cham. https://doi.org/10.1007/978-3-030-29736-7_20

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  • DOI: https://doi.org/10.1007/978-3-030-29736-7_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29735-0

  • Online ISBN: 978-3-030-29736-7

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