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dc.contributor.authorYang, Tzu-Chien_US
dc.contributor.authorChen, Sherry Y.en_US
dc.date.accessioned2020-10-05T02:01:59Z-
dc.date.available2020-10-05T02:01:59Z-
dc.date.issued1970-01-01en_US
dc.identifier.issn1049-4820en_US
dc.identifier.urihttp://dx.doi.org/10.1080/10494820.2020.1817759en_US
dc.identifier.urihttp://hdl.handle.net/11536/155399-
dc.description.abstractIndividual differences exist among learners. Among various individual differences, cognitive styles can strongly predict learners' learning behavior. Therefore, cognitive styles are essential for the design of online learning. There are a variety of cognitive style dimensions and overlaps exist among these dimensions. In particular, Witkin's field dependence/independence and Pask's Holism/Serialism share some similarities. To this end, it is necessary to develop a framework to show overlapped behavior between these two cognitive style dimensions. To address this issue, this study used the Lag Sequential Analysis to examine the overlaps between these two cognitive style dimensions from the aspect of online learning behavior. The results from this study indicated that the overlaps mainly appear in comprehensive/local and dynamic/fixed approaches. Based on the findings of this study, we develop a framework that can support the improvement of instruction design so that the needs of different cognitive style groups can be accommodated. Accordingly, this study is an interdisciplinary work, which makes scientific contributions to three communities, i.e. human-computer interaction, digital learning and learning analytic.en_US
dc.language.isoen_USen_US
dc.subjectLearning behavioren_US
dc.subjectcognitive styleen_US
dc.subjectself-regulated learningen_US
dc.subjectlearning analyticsen_US
dc.titleInvestigating students' online learning behavior with a learning analytic approach: field dependence/independence vs. holism/serialismen_US
dc.typeArticleen_US
dc.identifier.doi10.1080/10494820.2020.1817759en_US
dc.identifier.journalINTERACTIVE LEARNING ENVIRONMENTSen_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department教育研究所zh_TW
dc.contributor.department大數據研究中心zh_TW
dc.contributor.departmentInstitute of Educationen_US
dc.contributor.departmentBig Data Res Ctren_US
dc.identifier.wosnumberWOS:000570002000001en_US
dc.citation.woscount0en_US
Appears in Collections:Articles