Multi-Modalities in Classroom Learning Environments

Boulton, H; Brown, David J.; Standen, Penny; Belmonte, Matthew K.; Kwiatkowska, Gosia; Hughes-Roberts, Thomas; Taheri, Mohammad

doi:10.21125/inted.2018.0264

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…For example, data modalities were collected including gaze, electrodermal activity, movement and position data to predict the quality of collaborative problem solving . In another example by Boulton et al (2018), multiple data modalities were also captured including, facial expression, the position of the learner, and their affective state. Overall, we see that the utilization of the various data modalities is the key feature in allowing us to holistically capture students' interactions and identify in-depth insights into the learning processes and strategies.…”

Section: Data Modalities Used In Mmla Researchmentioning

confidence: 99%

The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review

Alwahaby¹,

Cukurova²,

Papamitsiou³

et al. 2021

Preprint

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There is a growing interest in the research and use of multimodal data in learning analytics. This paper presents a systematic literature review of multimodal learning analytics (MMLA) research to assess i) the available evidence of impact on learning outcomes in real-world contexts and ii) explore the extent to which ethical considerations are addressed. A few recent literature reviews argue for the promising value of multimodal data in learning analytics research. However, our understanding of the challenges associated with MMLA research from real-world teaching and learning environments is limited. To address this gap, this paper provides an overview of the evidence of impact and ethical considerations stemming from an analysis of the relevant MMLA research published in the last decade. The search of the literature resulted in 663 papers, of which 100 were included in the final synthesis. The results show that the evidence of real-world impact on learning outcomes is weak, and ethical aspects of MMLA work are rarely addressed. We discuss our results through the lenses of two theoretical frameworks for evidence of impact types and ethical dimensions of MMLA. We conclude that for MMLA to stay relevant and become part of mainstream education, future research should directly address the gaps identified in this review.

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Section: Data Modalities Used In Mmla Researchmentioning

confidence: 99%

The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review

Alwahaby¹,

Cukurova²,

Papamitsiou³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Teachers selected learning material from a library to create their own Learning activities and Learning graphs : an online equivalent of a specific lesson in traditional learning environments, where several learning goals are defined and are expected to be acquired. To reach these goals, the learning experience is divided into several Smart Learning Atoms which are representations of small pieces of knowledge (Boulton, et al ., 2019). These reusable learning objects are self‐contained learning components that are stored and accessed independently.…”

Section: Methodsmentioning

confidence: 99%

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

Standen¹,

Brown²,

Taheri³

et al. 2020

Brit J Educational Tech

Self Cite

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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.

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“…Baek and Aguilar (2023), for example, discuss learning analytics as a means to assess and improve the design of educational technologies for students with disabilities. By leveraging data-driven insights, including learning analytics for disabled students -such as multimodal learning analytics in tracking the students' experience or analytics able to capture students' emotions (see also Boulton et al, 2018) learning experiences for students with diverse disabilities could be substantially enhanced by creating more inclusive educational environments.…”

Section: Inclusive Designmentioning

confidence: 99%

Equity in Edtech by Design

Cermakova,

Prado,

Kucirkova

2024

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This report aims to provide guidance for improving equitable EdTech design, policy and practice. We identified relevant academic literature and captured best practices in identifying equitable EdTech features, as well as biassed design and organisational practices in EdTech. Our approach draws from existing literature indicating that accepted standards and indicators have generally proven to positively influence developer and consumer awareness, as well as policy-makers’ decision-making.

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Multi-Modalities in Classroom Learning Environments

Cited by 8 publications

References 12 publications

The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review

The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review

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

Equity in Edtech by Design

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