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dc.contributor.advisorChan, Philip K.
dc.contributor.authorMori, Makoto
dc.date.accessioned2016-01-08T16:40:39Z
dc.date.available2016-01-08T16:40:39Z
dc.date.issued2015-12
dc.identifier.urihttp://hdl.handle.net/11141/780
dc.descriptionThesis (M.S.) - Florida Institute of Technologyen_US
dc.description.abstractIn this study we investigate the correlation between student behavior and performance in online courses. Based on the web logs and syllabus of a course, we extract features that characterize student behavior. Using machine learning algorithms, we build models to predict performance at end of the period. Furthermore, we identify important behavior and behavior combinations in the models. The result of prediction in three tasks reach 87% accurate on average without using any score related features in the first half of the semester.en_US
dc.language.isoen_USen_US
dc.rightsCC BY Creative Commons with Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en_US
dc.subjectEducational data miningen_US
dc.subjectMachine learningen_US
dc.subjectOnline courseen_US
dc.subjectStudent learning behavioren_US
dc.subjectRandom forest with K-fold cross-validationen_US
dc.titleIdentifying Student Behavior for Improving Online Course Performance with Machine Learningen_US
dc.typeThesisen_US
thesis.degree.nameMaster of Science in Computer Scienceen_US
thesis.degree.levelMastersen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.departmentComputer Sciences and Cybersecurityen_US
thesis.degree.grantorFlorida Institute of Technologyen_US


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CC BY Creative Commons with Attribution
Except where otherwise noted, this item's license is described as CC BY Creative Commons with Attribution