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

50Citations
Citations of this article
245Readers
Mendeley users who have this article in their library.

This article is free to access.

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.

References Powered by Scopus

Generalized linear mixed models: a practical guide for ecology and evolution

6768Citations
N/AReaders
Get full text

Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions

3370Citations
N/AReaders
Get full text

Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC)

1304Citations
N/AReaders
Get full text

Cited by Powered by Scopus

A Review of Artificial Intelligence (AI) in Education from 2010 to 2020

433Citations
N/AReaders
Get full text

Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education

321Citations
N/AReaders
Get full text

AI-enabled adaptive learning systems: A systematic mapping of the literature

289Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Standen, P. J., Brown, D. J., Taheri, M., Galvez Trigo, M. J., Boulton, H., Burton, A., … 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), 1748–1765. https://doi.org/10.1111/bjet.13010

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 68

71%

Lecturer / Post doc 12

13%

Researcher 9

9%

Professor / Associate Prof. 7

7%

Readers' Discipline

Tooltip

Computer Science 29

39%

Social Sciences 23

31%

Psychology 13

18%

Arts and Humanities 9

12%

Save time finding and organizing research with Mendeley

Sign up for free