Open Universiteit

Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLee, M.J.W.-
dc.contributor.authorKirschner, Paul A.-
dc.contributor.authorKester, Liesbeth-
dc.identifier.citationLee, M. J. W., Kirschner, P. A., & Kester, L. (2016). Learning analytics in massively multi-user virtual environments and courses. Journal of Computer Assisted Learning, 32(3), 187-189. doi: 10.1111/jcal.12139en_US
dc.description.abstractThere is much ongoing interest in big data and the role it can play in decision-making in diverse areas of science, commerce and entertainment. By employing a combination of modern artificial intelligence, machine learning and statistics techniques, extremely large and complex data sets can be ‘mined’ in a variety of ways to reveal relationships, patterns and insights not easily discoverable through standard database management tools and data processing applications. In education, data mining approaches have been applied to the analysis of electronic stores or repositories of student data for a number of years now (Romero & Ventura, 2007), but this has been occurring largely at the institutional or sector level. Such applications, which are sometimes referred to as ‘academic analytics’ (Campbell, DeBlois, & Oblinger 2007; Goldstein & Katz, 2005), have not become mainstream, being relevant mainly to governments, funding agencies and institutional administrators rather than students and teachers (Siemens et al., 2011). More recently, a new field known as learning analytics (Long & Siemens, 2011; Siemens et al., 2011) has emerged that seeks to generate knowledge ‘about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs’ (Siemens, 2011, para. 5). This knowledge can be employed for a range of purposes, among which are to allow learners to reflect on their activity and progress in relation to that of others as well as to assist teachers and support staff in predicting, identifying and supporting learners who may require additional attention and intervention (Powell & MacNeill, 2012). Occurring in parallel is the burgeoningen_US
dc.publisherJournal of Computer Assisted Learningen_US
dc.subjectLearning analyticsen_US
dc.subjectVirtual environmentsen_US
dc.subjectVirtual coursesen_US
dc.titleLearning analytics in massively multi-user virtual environments and coursesen_US
Appears in Collections:1. FEEEL Publications, books and conference papers

Files in This Item:
File Description SizeFormat 
Learning analytics in massively multi-user virtual environments and courses.pdf122.79 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.