Ferguson, Rebecca and Buckingham Shum, Simon
This is the latest version of this eprint.
PDF (Version of Record)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
|DOI (Digital Object Identifier) Link:||https://doi.org/10.4018/978-1-4666-0300-4.ch017|
|Google Scholar:||Look up in Google Scholar|
This chapter examines the meaning of ‘open’ in terms of tools, resources and education, and goes on to explore the association between open approaches to education and the development of online social learning. It considers why this form of learning is emerging so strongly at this point, what its underlying principles are, and how it can be defined. Openness is identified as one of the motivating rationales for a social media space tuned for learning, called SocialLearn, which is currently being trialed at The Open University in the UK. SocialLearn has been designed to support online social learning by helping users to clarify their intention, ground their learning and engage in learning conversations. The emerging design concept and implementation are described here, with a focus on what personalization means in this context, and on how learning analytics could be used to provide different types of recommendation that support learning.
|Item Type:||Book Chapter|
|Keywords:||collaboration; distance learning; online social learning; learning analytics; open educational resources; OER; open SocialLearn|
|Academic Unit/School:||Institute of Educational Technology
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Interdisciplinary Research Centre:||Centre for Research in Education and Educational Technology (CREET)
Centre for Research in Computing (CRC)
|Depositing User:||Rebecca Ferguson|
|Date Deposited:||18 Apr 2012 13:38|
|Last Modified:||29 Nov 2016 17:51|
|Share this page:|
Available Versions of this Item
Download history for this item
These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.