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dc.contributor.authorYang, Christopher C.Y.en
dc.contributor.authorFlanagan, Brendanen
dc.contributor.authorAkçapınar, Gökhanen
dc.contributor.authorOgata, Hiroakien
dc.contributor.alternative緒方, 広明ja
dc.description[The 9th International Learning Analytics and Knowledge (LAK) Conference] March 4-8, 2019, Tempe, Arizona, USAen
dc.description.abstractThe increasing volume of student reading logs from virtual learning environment (VLE) provides opportunities for mining student’ engagement pattern in digital textbook reading. In order to mine and measure students’ engagement pattern, in this paper, we extract several students’ reading interaction variables from the digital textbook as metrics for the measurement of reading engagement. Moreover, in order to explore the presence of subpopulation of students that can be differentiated based on their engagement patterns and academic performances, we cluster students into different groups. Students are clustered based on their reading interactions such as total session of reading, total notes adding, etc. Accordingly, we identify students’ engagement patterns from different groups based on the clustering analysis results. Several student subpopulations such as low engagement high academic performances and low engagement low academic performances are identified based on students’ reading interaction characteristics by clustering analysis. The obtained results can be used to provide researchers with opportunities to intervene in the specific group of students and also an optimal choice for student grouping.en
dc.publisherSociety for Learning Analytics Research (SoLAR)en
dc.rightsThis work is published under the terms of the Creative Commons Attribution- Noncommercial-ShareAlike 3.0 Australia Licence.en
dc.subjectStudent engagement patternen
dc.subjectacademic performanceen
dc.subjectdigital textbooken
dc.titleInvestigating Subpopulation of Students in Digital Textbook Reading Logs by Clusteringen
dc.typeconference paper-
dc.type.niitypeConference Paper-
dc.identifier.jtitleCompanion Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK'19)-
dc.addressKyoto Universityen
dc.addressKyoto Universityen
dc.addressKyoto University・Hacettepe Universityen
dc.addressKyoto Universityen
dcterms.accessRightsopen access-
jpcoar.funderName.alternativeJapan Society for the Promotion of Science (JSPS)en
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