An evaluation of an adaptive learning system based on multimodal affect recognition for learners with intellectual disabilities

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Standen, PJ, Brown, DJ ORCID: https://orcid.org/0000-0002-1677-7485, Taheri, M ORCID: https://orcid.org/0000-0001-7594-4530, Galvez Trigo, MJ, Boulton, H ORCID: https://orcid.org/0000-0003-4671-0182, Burton, A ORCID: https://orcid.org/0000-0002-9073-8310, Hallewell, MJ, Lathe, JG, Shopland, N ORCID: https://orcid.org/0000-0003-2082-9070, Blanco Gonzalez, MA, Kwiatkowska, GM, Milli, E, Cobello, S, Mazzucato, A, Traversi, M and Hortal, E, 2020. An evaluation of an adaptive learning system based on multimodal affect recognition for learners with intellectual disabilities. British Journal of Educational Technology, 51 (5), pp. 1748-1765. ISSN 0007-1013

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Official URL: https://doi.org/10.1111/bjet.13010

Abstract

Artificial intelligence tools for education (AIEd) have been used to automate the provision of learning support to mainstream learners. One of the most innovative approaches in this field is the use of data and machine learning for the detection of a student's affective state, to move them out of negative states that inhibit learning, into positive states such as engagement. In spite of their obvious potential to provide the personalisation that would give extra support for learners with intellectual disabilities, little work on AIEd systems that utilise affect recognition currently addresses this group. Our system used multimodal sensor data and machine learning to first identify three affective states linked to learning (engagement, frustration, boredom) and second determine the presentation of learning content so that the learner is maintained in an optimal affective state and rate of learning is maximised. To evaluate this adaptive learning system, 67 participants aged between 6 and 18 years acting as their own control took part in a series of sessions using the system. Sessions alternated between using the system with both affect detection and learning achievement to drive the selection of learning content (intervention) and using learning achievement alone (control) to drive the selection of learning content. Lack of boredom was the state with the strongest link to achievement, with both frustration and engagement positively related to achievement. There was significantly more engagement and less boredom in intervention than control sessions, but no significant difference in achievement. These results suggest that engagement does increase when activities are tailored to the personal needs and emotional state of the learner and that the system was promoting affective states that in turn promote learning. However, longer exposure is necessary to determine the effect on learning.

Item Type:	Journal article
Publication Title:	British Journal of Educational Technology
Creators:	Standen, P.J., Brown, D.J., Taheri, M., Galvez Trigo, M.J., Boulton, H., Burton, A., Hallewell, M.J., Lathe, J.G., Shopland, N., Blanco Gonzalez, M.A., Kwiatkowska, G.M., Milli, E., Cobello, S., Mazzucato, A., Traversi, M. and Hortal, E.
Publisher:	Wiley
Date:	September 2020
Volume:	51
Number:	5
ISSN:	0007-1013
Identifiers:	Number Type 10.1111/bjet.13010 DOI 1352501 Other
Rights:	© 2020 The Authors. British Journal of Educational Technology published by John Wiley & Sons Ltd on behalf of British Educational Research Association. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Divisions:	Schools > School of Science and Technology Schools > Nottingham Institute of Education
Record created by:	Linda Sullivan
Date Added:	28 Sep 2020 10:09
Last Modified:	31 May 2021 15:16
URI:	https://irep.ntu.ac.uk/id/eprint/40974