Selection of learning analytics projects by using spherical fuzzy TOPSIS

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dc.authoridOnar, Sezi Cevik/0000-0001-6451-6709|kahraman, cengiz/0000-0001-6168-8185
dc.authorwosidOnar, Sezi Cevik/B-4146-2015
dc.authorwosidkahraman, cengiz/N-9259-2013
dc.contributor.authorOnart, Sezi Cevik
dc.contributor.authorKahraman, Cengiz
dc.contributor.authorOztaysi, Basar
dc.contributor.authorOtay, Irem
dc.date.accessioned2024-07-18T20:52:10Z
dc.date.available2024-07-18T20:52:10Z
dc.date.issued2020
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description15th Symposium of Intelligent Systems and Knowledge Engineering (ISKE) held jointly with 14th International FLINS Conference (FLINS) -- AUG 18-21, 2020 -- Cologne, GERMANYen_US
dc.description.abstractLearning analytics is the measurement of student progress by the collecting, analysis and reporting data in the learning environment. Learning analytics methods try to find out the dependent pattern in dataset gathered. Learning Analytics improve student outcomes in several ways, first, using learning analytics leads to measure student success correctly. With this information students can find accurate teaching techniques and support themselves. Also, it gives proper and faster feedback about learning technique to the stakeholders (principals, teachers, parents). The scope and the aim of learning analytic projects may differ for different organizations. Selecting the right learning analytic project is crucial for the overall success of the learning process. Despite their benefits, while selecting the learning analytic projects not only financial benefits but also various factors including Privacy, Access, Transparency, Security, Accuracy, Restrictions, and Ownership should be taken into account. Yet, evaluating these factors is not easy since they involve uncertainties. Therefore, the selection of learning analytic projects is a complex process that includes various uncertainties. In this study, we utilize an Spherical fuzzy TOPSIS approach for selecting Learning Analytics Projects This method enabled us defining the uncertainties with independent parameters.en_US
dc.description.sponsorshipFern Univ,TH Koln Univ Appl Sci,Univ Technol Sydney,SW Jiaotong Univ,Shunde Polytechn,Minnan Normal Univ,Natl Assoc Non Class Log & Computat Chinaen_US
dc.identifier.endpage234en_US
dc.identifier.isbn978-981-122-333-4
dc.identifier.startpage226en_US
dc.identifier.urihttps://hdl.handle.net/11411/8526
dc.identifier.volume12en_US
dc.identifier.wosWOS:000656123200028en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherWorld Scientific Publ Co Pte Ltden_US
dc.relation.ispartofDevelopments of Artificial Intelligence Technologies in Computation and Roboticsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLearning Analyticsen_US
dc.subjectSpherical Fuzzy Topsısen_US
dc.subjectLearning Technologiesen_US
dc.titleSelection of learning analytics projects by using spherical fuzzy TOPSISen_US
dc.typeConference Objecten_US

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