Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/28553
Title: Technology Enhanced Mulsemedia Learning (TEML) for Learners with Dyslexia
Authors: Mohana, M
Da Silveira, AC
Suvetha, V
Subashini, P
Ghinea, G
Santos, CAS
Keywords: mulsemedia;technology enhanced mulsemedia learning;quality of experiences;dyslexia;learning disability
Issue Date: 12-Jun-2023
Publisher: Association for Computing Machinery
Citation: Mohana, M. et al. (2023) 'Technology Enhanced Mulsemedia Learning (TEML) for Learners with Dyslexia', IMXw '23: Proceedings of the 2023 ACM International Conference on Interactive Media Experiences Workshops, Nantes, France, 12-15 June, pp. 62 - 65. doi: 10.1145/3604321.3604357.
Abstract: Mulsemedia-multiple sensorial media- includes both traditional multimedia and human sensorial effects, which gives immersive real-world experience. The booming of new technology and wearable devices creates a new avenue for Mulsemedia research in education and has brought new opportunities for Technology Enhanced Mulsemedia Learning (TEML). Traditional methods focus on unisensory experiences, even though the surrounding environment is multisensory. Moreover, this type of learning helps to stimulate various sensory channels to reinforce the learning process, which can also more benefit individuals with a variety of learning disabilities particularly dyslexia which affects a person's ability to read. Moreover, TEML helps to obtain 21st-century learning skills such as cognitive, productivity, social-cultural, metacognitive, and technological dimensions. This paper proposes TEML for learners with dyslexia, for that, this study focuses on an approach for identifying methods for dyslexia, a method for designing Mulsemedia devices using IoT technology, and an evaluation method for performing quality of experience (QoS) using questionnaire, and physiological signals such as GSR (Galvanic Skin Responses), and EEG (Electroencephalography). Those methods help to analyze the learner's emotional responses while experiencing Mulsemedia content.
URI: https://bura.brunel.ac.uk/handle/2438/28553
DOI: https://doi.org/10.1145/3604321.3604357
ISBN: 979-8-4007-0845-9
Other Identifiers: ORCiD: Gheorghita Ghinea https://orcid.org/0000-0003-2578-5580
Appears in Collections:Dept of Computer Science Research Papers

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