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Light-Bulb Moment?: Towards Adaptive Presentation of Feedback based on Students' Affective State

Published: 18 March 2015 Publication History

Abstract

Affective states play a significant role in students' learning behaviour. Positive affective states can enhance learning, whilst negative affective states can inhibit it. This paper describes a Wizard-of-Oz study which investigates whether the way feedback is presented should change according to the affective state of a student, in order to encourage affect change if that state is negative. We presented 'high-interruptive' feedback in the form of pop-up windows in which messages were immediately viewable; or 'low-interruptive' feedback, a glowing light bulb which students needed to click in order to access the messages. Our results show that when students are confused or frustrated high-interruptive feedback is more effective, but when students are enjoying their activity, there is no difference. Based on the results, we present guidelines for adaptively tailoring the presentation of feedback based on students' affective states when interacting with learning environments.

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    cover image ACM Conferences
    IUI '15: Proceedings of the 20th International Conference on Intelligent User Interfaces
    March 2015
    480 pages
    ISBN:9781450333061
    DOI:10.1145/2678025
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 18 March 2015

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    Author Tags

    1. adaptive feedback presentation
    2. affect
    3. emotions

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    IUI '15 Paper Acceptance Rate 47 of 205 submissions, 23%;
    Overall Acceptance Rate 746 of 2,811 submissions, 27%

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    • (2023)Complexity of Academic Emotions in Online Video-Based Learning: Implications for Asian LearnersInternational Handbook on Education Development in the Asia-Pacific10.1007/978-981-19-6887-7_52(1539-1556)Online publication date: 16-Nov-2023
    • (2023)Complexity of Academic Emotions in Online Video-Based Learning: Implications for Asian LearnersInternational Handbook on Education Development in Asia-Pacific10.1007/978-981-16-2327-1_52-1(1-18)Online publication date: 13-May-2023
    • (2022)I Don't Usually Listen, I Read: How Different Learner Groups Process Game FeedbackProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517480(1-15)Online publication date: 29-Apr-2022
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    • (2021)Adapting Software with Affective Computing: A Systematic ReviewIEEE Transactions on Affective Computing10.1109/TAFFC.2019.290237912:4(883-899)Online publication date: 1-Oct-2021
    • (2020)An Academic Affect Dataset: Spontaneous Facial Expressions and Head poses Collected during Online Examination2020 The 4th International Conference on E-Society, E-Education and E-Technology10.1145/3421682.3421692(83-87)Online publication date: 15-Aug-2020
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    • (2018)Opening Up an Intelligent Tutoring System Development Environment for Extensible Student ModelingArtificial Intelligence in Education10.1007/978-3-319-93843-1_13(169-183)Online publication date: 20-Jun-2018
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