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    Affective learning: improving engagement and enhancing learning with affect-aware feedback

    Grawemeyer, Beate and Mavrikis, M. and Holmes, W. and Gutierrez, Sergio and Wiedmann, M. and Rummel, N. (2017) Affective learning: improving engagement and enhancing learning with affect-aware feedback. User Modeling and User-Adapted Interaction - Special Issue on Impact of Learner Modeling 27 (1), pp. 119-158. ISSN 0924-1868.

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    Abstract

    This paper describes the design and ecologically valid evaluation of a learner model that lies at the heart of an intelligent learning environment called iTalk2Learn. A core objective of the learner model is to adapt formative feedback based on students' affective states. Types of adaptation include what type of formative feedback should be provided and how it should be presented. Two Bayesian networks trained with data gathered in a series of Wizard-of-Oz studies are used for the adaptation process. This paper reports results from a quasi-experimental evaluation, in authentic classroom settings, which compared a version of iTalk2Learn that adapted feedback based on students' affective states as they were talking aloud with the system (the affect condition) with one that provided feedback based only on the students' performance (the non-affect condition). Our results suggest that affect-aware support contributes to reducing boredom and off-task behavior, and may have an effect on learning. We discuss the internal and ecological validity of the study, in light of pedagogical considerations that informed the design of the two conditions. Overall, the results of the study have implications both for the design of educational technology and for classroom approaches to teaching, because they highlight the important role that affect-aware modelling plays in the adaptive delivery of formative feedback to support learning.

    Metadata

    Item Type: Article
    Additional Information: The final publication is available at Springer via the link above.
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Beate Grawemeyer
    Date Deposited: 09 Jan 2017 16:04
    Last Modified: 08 Feb 2018 01:10
    URI: http://eprints.bbk.ac.uk/id/eprint/16676

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