Title: Social learning graphs: combining social network graphs and analytics to represent learning experiences

Authors: Abelardo Pardo

Addresses: School of Electrical and Information Engineering, The University of Sydney, NSW 2006, Australia

Abstract: The web is evolving towards higher levels of personalisation, and closer interaction through social networking sites. The wealth of available data about user interactions has prompted the appearance of highly personalised tools for social interaction. Learning experiences are following a parallel evolution, and they need more effective personalisation and a strong social component. Semantic web was presented as the solution to achieve a highly personalised learning experience. Analogously, social networks are used to represent relations among students. But a simple and common representation of these two perspectives in the context of a learning environment is missing. In this paper, Social Learning Graphs are presented as a framework to capture and represent the interactions and relations occurring among multiple entities in a learning environment. The advantage of this representation is that it combines structural in formation with observations in a common graph notation suitable to be used by procedures such as link-prediction, recommendation, and abstractions.

Keywords: social networks; semantic web; intelligent leaning; environments; learning analytics; social network graphs; learning experience; personalisation; social media.

DOI: 10.1504/IJSMILE.2013.051652

International Journal of Social Media and Interactive Learning Environments, 2013 Vol.1 No.1, pp.43 - 58

Available online: 21 Jan 2013

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