Learning analytics in massively multi-user virtual environments and courses

M.J.W. Lee, Paul A. Kirschner, Liesbeth Kester

Research output: Contribution to journalEditorialAcademicpeer-review

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, datamining approaches have been applied to the analysis of electronic stores or repositories of student data for anumber of years now (Romero & Ventura, 2007), but this has been occurring largely at the institutional or sector level. Such applications, which are sometimes referred toas ‘academic analytics’ (Campbell, DeBlois, & Oblinger2007; Goldstein & Katz, 2005), have not become mainstream, being relevant mainly to governments,funding agencies and institutional administrators ratherthan students and teachers (Siemens et al., 2011). Morerecently, a new field known as learning analytics (Long& Siemens, 2011; Siemens et al., 2011) has emerged thatseeks to generate knowledge ‘about learners and theircontexts, for purposes of understanding and optimizinglearning and the environments in which it occurs’(Siemens, 2011, para. 5). This knowledge can beemployed for a range of purposes, among which are toallow learners to reflect on their activity and progress inrelation to that of others as well as to assist teachers andsupport staff in predicting, identifying and supportinglearners who may require additional attention andintervention (Powell & MacNeill, 2012).
Original languageEnglish
Pages (from-to)187-189
JournalJournal of Computer Assisted Learning
Volume32
Issue number3
DOIs
Publication statusPublished - 24 Mar 2016

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Virtual reality
Students
learning
Artificial intelligence
Learning systems
Education
Decision making
Statistics
artificial intelligence
teacher
commerce
entertainment
student
funding
statistics
electronics
staff
decision making
science
management

Keywords

  • Learning analytics
  • Virtual environments
  • Virtual courses

Cite this

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title = "Learning analytics in massively multi-user virtual environments and courses",
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, datamining approaches have been applied to the analysis of electronic stores or repositories of student data for anumber of years now (Romero & Ventura, 2007), but this has been occurring largely at the institutional or sector level. Such applications, which are sometimes referred toas ‘academic analytics’ (Campbell, DeBlois, & Oblinger2007; Goldstein & Katz, 2005), have not become mainstream, being relevant mainly to governments,funding agencies and institutional administrators ratherthan students and teachers (Siemens et al., 2011). Morerecently, a new field known as learning analytics (Long& Siemens, 2011; Siemens et al., 2011) has emerged thatseeks to generate knowledge ‘about learners and theircontexts, for purposes of understanding and optimizinglearning and the environments in which it occurs’(Siemens, 2011, para. 5). This knowledge can beemployed for a range of purposes, among which are toallow learners to reflect on their activity and progress inrelation to that of others as well as to assist teachers andsupport staff in predicting, identifying and supportinglearners who may require additional attention andintervention (Powell & MacNeill, 2012).",
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Learning analytics in massively multi-user virtual environments and courses. / Lee, M.J.W.; Kirschner, Paul A.; Kester, Liesbeth.

In: Journal of Computer Assisted Learning, Vol. 32, No. 3, 24.03.2016, p. 187-189.

Research output: Contribution to journalEditorialAcademicpeer-review

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