Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/26065
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dc.contributor.authorIdrizi, Ermiraen_US
dc.contributor.authorFiliposka, Sonjaen_US
dc.contributor.authorTrajkovik, Vladimiren_US
dc.date.accessioned2023-03-09T09:23:05Z-
dc.date.available2023-03-09T09:23:05Z-
dc.date.issued2021-06-28-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/26065-
dc.description.abstractThis article examines the impact of personality traits, learning styles, gender, and online course factors (course difficulty, group affiliation, provided materials, etc.) in the academic success of students taking online courses and their overall success rate through traditional classes. Students’ performance in the online learning environment is still a new perception, and a fair numbers of details are still unknown, in stark contrast to the details known in regard to traditional learning methods. Different types of learners respond differently to online and traditional courses. A case study was performed in which students were asked to attend two online courses, with different difficulty levels, during one semester. One-way analysis of variance was used to determine which factors are significant for the academic performance of students taking online courses, as well as for their overall academic success. Findings from the case study indicate that female students score slightly better, course difficulty has impact on test results, emotional students are more susceptible to online environments, and learning styles are more difficult to identify in online classes.en_US
dc.publisherÉruditen_US
dc.relation.ispartofInternational Review of Research in Open and Distributed Learningen_US
dc.subjectonline education, character traits, learning styles, academic success, genderen_US
dc.titleAnalysis of success indicators in online learningen_US
dc.typeArticleen_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles
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