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Ferguson, Rebecca and Buckingham Shum, Simon
(2012).
DOI: https://doi.org/10.1145/2330601.2330616
URL: http://dl.acm.org/citation.cfm?id=2330616&dl=ACM&c...
Abstract
This paper proposes that Social Learning Analytics (SLA) can be usefully thought of as a subset of learning analytics approaches. SLA focuses on how learners build knowledge together in their cultural and social settings. In the context of online social learning, it takes into account both formal and informal educational environments, including networks and communities. The paper introduces the broad rationale for SLA by reviewing some of the key drivers that make social learning so important today. Five forms of SLA are identified, including those which are inherently social, and others which have social dimensions. The paper goes on to describe early work towards implementing these analytics on SocialLearn, an online learning space in use at the UK’s Open University, and the challenges that this is raising. This work takes an iterative approach to analytics, encouraging learners to respond to and help to shape not only the analytics but also their associated recommendations.
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About
- Item ORO ID
- 32910
- Item Type
- Conference or Workshop Item
- ISBN
- 1-4503-1111-3, 978-1-4503-1111-3
- Extra Information
-
This is the author’s version of the work. It is posted here
by permission of ACM for your personal use. Not for redistribution. The definitive version was published in:
Proceedings LAK’12: 2nd International Conference on Learning
Analytics & Knowledge, 29 April - 2 May 2012, Vancouver, BC,
Canada. ACM Digital Library: http://dl.acm.org - Keywords
- social learning; learning analytics; discourse analytics; learning how to learn; transferable skills; 21st century skills; educational assessment; social learning analytics; SocialLearn
- Academic Unit or School
-
Institute of Educational Technology (IET)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM) - Research Group
- Centre for Research in Computing (CRC)
- Copyright Holders
- © 2012 ACM
- Related URLs
- Depositing User
- Kay Dave