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Autor/inn/enHolmes, Wayne; Nguyen, Quan; Zhang, Jingjing; Mavrikis, Manolis; Rienties, Bart
TitelLearning Analytics for Learning Design in Online Distance Learning
QuelleIn: Distance Education, 40 (2019) 3, S.309-329 (21 Seiten)Infoseite zur Zeitschrift
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ZusatzinformationORCID (Holmes, Wayne)
ORCID (Zhang, Jingjing)
ORCID (Mavrikis, Manolis)
ORCID (Rienties, Bart)
Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN0158-7919
DOI10.1080/01587919.2019.1637716
SchlagwörterLearning Analytics; Instructional Design; Electronic Learning; Distance Education; Social Networks; Network Analysis; Teaching Methods; Virtual Classrooms; Foreign Countries; Open Universities; College Students; United Kingdom
AbstractThere has been a growing interest in how teaching might be informed by "learning design" (LD), with a promising method for investigating LD being offered by the emerging field of "learning analytics" (LA). In this study, we used a novel LA for LD methodology to investigate the implementation of LD in an online distance learning context. A key innovation is the focus on "patterns" of LD. Using data from the virtual learning environment, outcomes data, and self-reports, for 47,784 students, we investigated the impact of those patterns on student behaviour, pass rates and satisfaction. A second innovation involves "social network analysis." Our study revealed that different patterns of LD were associated with statistically significant differences in behaviour, but not in pass rates or satisfaction. Nonetheless, the study highlights that applying LA to LD might, in a virtuous circle, contribute to the validity and effectiveness of both, and to the enhancement of online distance learning. (As Provided).
AnmerkungenRoutledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2020/1/01
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