Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1044
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dc.contributor.authorWijeratne, M. D-
dc.contributor.authorLakmal, R. H. G. A-
dc.contributor.authorGeethadhari, W. K. S-
dc.contributor.authorAthalage, M. A-
dc.contributor.authorGamage, A-
dc.contributor.authorKasthurirathna, D-
dc.date.accessioned2022-02-09T04:34:28Z-
dc.date.available2022-02-09T04:34:28Z-
dc.date.issued2021-04-21-
dc.identifier.citationM. D. Wijeratne, R. H. G. A. Lakmal, W. K. S. Geethadhari, M. A. Athalage, A. Gamage and D. Kasthurirathna, "Computer Vision and NLP based Multimodal Ensemble Attentiveness Detection API for E-Learning," 2021 IEEE Global Engineering Education Conference (EDUCON), 2021, pp. 814-820, doi: 10.1109/EDUCON46332.2021.9453978.en_US
dc.identifier.issn2165-9567-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1044-
dc.description.abstractAttention is the fundamental element of effective learning, memory, and interaction. Learning however, with the evolvement of technologies in the modern digital age, has surpassed traditional learning systems to more convenient online or e-learning systems. Nevertheless, unlike in the traditional learning systems, attention detection of a student in an e-learning environment remains one of the barely explored areas in Human Computer Interaction. This study proposes a multimodal ensemble solution to detect the level of attentiveness of a student in an e-learning environment, with the use of computer vision, natural language processing, and deep learning to overcome the barriers in identifying user attention in e-learning. The proposed multimodal captures, processes, and predicts user attentiveness levels of individual students, which are subsequently aggregated through an ensemble model to derive an overall outcome of better accuracy than individual model outcomes. The final outcome of the ensemble model produces a range of percentages, within which the attentiveness level of the student lies during a single online lesson. This range is consequently delivered to the users through an Application Programming Interface.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2021 IEEE Global Engineering Education Conference (EDUCON);Pages 814-820-
dc.subjectComputer Visionen_US
dc.subjectNLP based Multimodalen_US
dc.subjectEnsemble Attentiveness Detectionen_US
dc.subjectAttentiveness Detection APIen_US
dc.subjectE-Learningen_US
dc.titleComputer Vision and NLP based Multimodal Ensemble Attentiveness Detection API for E-Learningen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/EDUCON46332.2021.9453978en_US
Appears in Collections:Department of Computer Science and Software Engineering-Scopes
Research Papers - IEEE
Research Papers - IEEE
Research Papers - SLIIT Staff Publications
Research Papers - SLIIT Staff Publications
Research Publications -Dept of Information Technology

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