When the Going Gets Tough: Students’ Perceptions on Affect-Aware Support in an Exploratory Learning Environment for Fractions

Beate Grawemeyer, Manolis Mavrikis, Wayne Holmes

    Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

    Abstract

    It is well understood that affect interacts with and influences the learning process [2, 7, 9]. The impact of affect on learning is not straightforward. For example, D’Mello et al. explore how confusion, which superficially might be considered a negative affective state, is likely to promote learning under appropriate conditions [3]. In addition, the way the students perceive the tasks and the support they receive can impact their experience, agency and self-efficacy which in turn has been shown to relate to persistence and long-term outcomes [1]. It is important therefore, to deepen our understanding of the role of affect in learning in general and in particular students’ perceptions of their own learning with digital environments that provide feedback.

    In previous work [4] we described the development of affect-aware support and the effect of such support in relation to learning during a whole classroom intervention with a sequence of fraction learning tasks within the iTalk2Learn platform. In contrast, in this paper, we report on a study that asked students to self-report their affective states while undertaking fractions tasks.
    Original languageEnglish
    Title of host publicationArtificial Intelligence in Education.
    Subtitle of host publicationPosters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium, Proceedings, Part II
    Editors Maria Mercedes Rodrigo, Noburu Matsuda, Alexandra I. Cristea, Vania Dimitrova
    PublisherSpringer, Cham
    Pages483-487
    Number of pages5
    Volume13356
    ISBN (Electronic)978-3-031-11647-6
    ISBN (Print)978-3-031-11646-9
    DOIs
    Publication statusE-pub ahead of print - 26 Jul 2022
    Event23rd International Conference on Artificial Intelligence in Education, AIED 2022 - Durham, United Kingdom
    Duration: 27 Jul 202231 Jul 2022
    https://link.springer.com/book/10.1007/978-3-031-11647-6

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer
    Volume13356
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference23rd International Conference on Artificial Intelligence in Education, AIED 2022
    Abbreviated titleAIED 2022.
    Country/TerritoryUnited Kingdom
    CityDurham
    Period27/07/2231/07/22
    Internet address

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