TOWARDS WEBCAM-BASED FACE DIRECTION TRACKING to DETECT LEARNERS' ATTENTION within ASYNCHRONOUS E-LEARNING ENVIRONMENT

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dc.contributor.authorRamaha, N.T.A.
dc.contributor.authorKaras, A.R.
dc.contributor.authorGül, E.
dc.contributor.authorBozkurt, M.R.
dc.contributor.authorYayvan, R.
dc.date.accessioned2024-09-29T16:16:03Z
dc.date.available2024-09-29T16:16:03Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description6th International Conference on Smart City Applications -- 27 October 2021 through 29 October 2021 -- Safranbolu -- 175815en_US
dc.description.abstractRecently, as a consequence of COVID-19 pandemic, the delivery of education at most of the educational institutions depended mainly on e-learning. So, the researchers give more attention for both synchronous and asynchronous e-learning. Although from an economical perspective, asynchronous e-learning seems to be the best e-learning option for institutions, still one of the biggest challenges is how to keep learners motivated for the entire learning process. One of important motivational factors that drives the success of the learning process is the learner attention. Therefore, to retain the learners' attention during the asynchronous e-learning process, we need first to detect their loss of attention. Accordingly, more studies started to focus on detecting learners' attention. However, those studies can't be widely used for attention detection within asynchronous e-learning environments, as the used approaches tend to be inaccurate, and complex for the design and maintain. In contrast, in this study, we explore the possibility to find a simple way that can be widely used to detect learners' attention within the asynchronous e-learning environments. Therefore, we used webcams which are available in almost every laptop, and computer vision tools to detect learners' attention by tracking their faces. Thereafter, we evaluated the accuracy of our suggested method, the result of this evaluation showed that our method is efficient. © Author(s) 2021. CC BY 4.0 License.en_US
dc.identifier.doi10.5194/isprs-Archives-XLVI-4-W5-2021-445-2021
dc.identifier.endpage449en_US
dc.identifier.issn1682-1750
dc.identifier.issue4/W5-2021en_US
dc.identifier.scopus2-s2.0-85122322062en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage445en_US
dc.identifier.urihttps://doi.org/10.5194/isprs-Archives-XLVI-4-W5-2021-445-2021
dc.identifier.urihttps://hdl.handle.net/20.500.14619/8832
dc.identifier.volume46en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInternational Society for Photogrammetry and Remote Sensingen_US
dc.relation.ispartofInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archivesen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAsynchronous e-learningen_US
dc.subjectAttention Detection.en_US
dc.subjectComputer Visionen_US
dc.subjectCOVID-19en_US
dc.subjectE-Learningen_US
dc.subjectFace Directionen_US
dc.titleTOWARDS WEBCAM-BASED FACE DIRECTION TRACKING to DETECT LEARNERS' ATTENTION within ASYNCHRONOUS E-LEARNING ENVIRONMENTen_US
dc.typeConference Objecten_US

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