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dc.contributor.authorLiu, Shuangyan
dc.contributor.authord’Aquin, Mathieu
dc.date.accessioned2017-10-09T10:47:10Z
dc.date.available2017-10-09T10:47:10Z
dc.date.issued2017-04-25
dc.identifier.citationLiu, Shuangyan, & d'Aquin, Mathieu. (2017). Unsupervised learning for understanding student achievement in a distance learning setting. Paper presented at the IEEE Global Engineering Education Conference (EDUCON), Athens. doi: 10.1109/EDUCON.2017.7943026en_IE
dc.identifier.urihttp://hdl.handle.net/10379/6890
dc.description.abstractMany factors could affect the achievement of students in distance learning settings. Internal factors such as age, gender, previous education level and engagement in online learning activities can play an important role in obtaining successful learning outcomes, as well as external factors such as regions where they come from and the learning environment that they can access. Identifying the relationships between student characteristics and distance learning outcomes is a central issue in learning analytics. This paper presents a study that applies unsupervised learning for identifying how demographic characteristics of students and their engagement in online learning activities can affect their learning achievement. We utilise the K-Prototypes clustering method to identify groups of students based on demographic characteristics and interactions with online learning environments, and also investigate the learning achievement of each group. Knowing these groups of students who have successful or poor learning outcomes can aid faculty for designing online courses that adapt to different students' needs. It can also assist students in selecting online courses that are appropriate to them.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherIEEEen_IE
dc.relation.ispartofGlobal Engineering Education Conference (EDUCON), 2017 IEEEen
dc.subjectStudent behaviouren_IE
dc.subjectStudent trajectoryen_IE
dc.subjectLearning analyticsen_IE
dc.subjectUnsupervised learningen_IE
dc.subjectCluster analysisen_IE
dc.subjectKPrototypes algorithmen_IE
dc.subjectOpen learning analytics datasetsen_IE
dc.subjectDistance learningen_IE
dc.titleUnsupervised learning for understanding student achievement in a distance learning settingen_IE
dc.typeConference Paperen_IE
dc.date.updated2017-10-05T10:43:56Z
dc.identifier.doi10.1109/EDUCON.2017.7943026
dc.local.publishedsourcehttp://dx.doi.org/10.1109/EDUCON.2017.7943026en_IE
dc.description.peer-reviewedpeer-reviewed
dc.internal.rssid13252788
dc.local.contactMathieu D'Aquin. Email: mathieu.daquin@nuigalway.ie
dc.local.copyrightcheckedYes already in other open access repository
dc.local.versionACCEPTED
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