Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/28553
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dc.contributor.authorMohana, M-
dc.contributor.authorDa Silveira, AC-
dc.contributor.authorSuvetha, V-
dc.contributor.authorSubashini, P-
dc.contributor.authorGhinea, G-
dc.contributor.authorSantos, CAS-
dc.date.accessioned2024-03-16T19:46:14Z-
dc.date.available2024-03-16T19:46:14Z-
dc.date.issued2023-06-12-
dc.identifierORCiD: Gheorghita Ghinea https://orcid.org/0000-0003-2578-5580-
dc.identifier.citationMohana, 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.en_US
dc.identifier.isbn979-8-4007-0845-9-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/28553-
dc.description.abstractMulsemedia-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.en_US
dc.description.sponsorshipThis work is supported by the Centre for Machine Learning and Intelligence (CMLI) funded by the Department of Science and Technology (DST-CURIE).en_US
dc.format.extent62 - 65-
dc.format.mediumElectronic-
dc.language.isoen_USen_US
dc.publisherAssociation for Computing Machineryen_US
dc.rightsCopyright © 2023 The Authors. Published by ACM under exclusive license. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in IMXw '23: Proceedings of the 2023 ACM International Conference on Interactive Media Experiences Workshops, https://doi.org/10.1145/3604321.3604357 (see: https://www.acm.org/publications/policies/publication-rights-and-licensing-policy).-
dc.rights.urihttps://www.acm.org/publications/policies/publication-rights-and-licensing-policy-
dc.subjectmulsemediaen_US
dc.subjecttechnology enhanced mulsemedia learningen_US
dc.subjectquality of experiencesen_US
dc.subjectdyslexiaen_US
dc.subjectlearning disabilityen_US
dc.titleTechnology Enhanced Mulsemedia Learning (TEML) for Learners with Dyslexiaen_US
dc.typeConference Paperen_US
dc.identifier.doihttps://doi.org/10.1145/3604321.3604357-
dc.relation.isPartOfACM International Conference Proceeding Series-
pubs.publication-statusPublished-
dc.rights.holderThe Authors-
Appears in Collections:Dept of Computer Science Research Papers

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