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    Affecting off-task behaviour: how affect-aware feedback can improve student learning

    Grawemeyer, Beate and Mavrikis, M. and Holmes, W. and Gutierrez-Santos, Sergio and Wiedmann, M. and Rummel, N. (2016) Affecting off-task behaviour: how affect-aware feedback can improve student learning. In: UNSPECIFIED (ed.) Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK '16. New York, U.S.: Association for Computing Machinery, pp. 104-113. ISBN 9781450341905.

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    Abstract

    This paper describes the development and evaluation of an affect-aware intelligent support component that is part of a learning environment known as iTalk2Learn. The intelligent support component is able to tailor feedback according to a student's affective state, which is deduced both from speech and interaction. The affect prediction is used to determine which type of feedback is provided and how that feedback is presented (interruptive or non-interruptive). The system includes two Bayesian networks that were trained with data gathered in a series of ecologically-valid Wizard-of-Oz studies, where the effect of the type of feedback and the presentation of feedback on students' affective states was investigated. This paper reports results from an experiment that compared a version that provided affect-aware feedback (affect condition) with one that provided feedback based on performance only (non-affect condition). Results show that students who were in the affect condition were less bored and less off-task, with the latter being statically significant. Importantly, students in both conditions made learning gains that were statistically significant, while students in the affect condition had higher learning gains than those in the non-affect condition, although this result was not statistically significant in this study's sample. Taken all together, the results point to the potential and positive impact of affect-aware intelligent support.

    Metadata

    Item Type: Book Section
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Administrator
    Date Deposited: 21 Jul 2016 09:05
    Last Modified: 31 Jan 2017 11:04
    URI: http://eprints.bbk.ac.uk/id/eprint/15764

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