Onart, Sezi CevikKahraman, CengizOztaysi, BasarOtay, Irem2024-07-182024-07-182020978-981-122-333-4https://hdl.handle.net/11411/852615th Symposium of Intelligent Systems and Knowledge Engineering (ISKE) held jointly with 14th International FLINS Conference (FLINS) -- AUG 18-21, 2020 -- Cologne, GERMANYLearning 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.eninfo:eu-repo/semantics/closedAccessLearning AnalyticsSpherical Fuzzy TopsısLearning TechnologiesConference Object23422612N/AWOS:000656123200028