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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/8955
Title: Learning analytics in massively multi-user virtual environments and courses
Authors: Lee, M.J.W.
Kirschner, Paul A.
Kester, Liesbeth
Keywords: Learning analytics
Virtual environments
Virtual courses
Issue Date: 2016
Publisher: Journal of Computer Assisted Learning
Citation: Lee, 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.12139
Abstract: There 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 burgeoning
URI: http://hdl.handle.net/1820/8955
Appears in Collections:1. FEEEL Publications, books and conference papers

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