Group Admins
Description
The true value and impact of educational recommender systems is not yet fully explored, with the exception of some work around the mechanistic nature of systems and the challenges posed by their use in education (Manouselis, 2012). However, we are largely unaware of knowing user behaviour and influences behind their selection and use, the value judgements that are being made and how we can learn from this engagement for future adoption. Understanding user behaviour and the correlation with user experience is fundamental to effective development, visibility and sustainability of ROER. Fully understanding and exploiting the potential impact of paradata can be important in supporting resource use and impact.
According to Campbell and Barker (2013), paradata ‘is a form of metadata that records how, and in what context, a learning resource is used … paradata records the opinion of the users…’.Thus paradata can record those interactions afforded by recommender systems such as sharing, liking, commenting, tagging, etc. but it can also go further in contextualising resource use through online comments. The application of these elements within ROER is an area in which the author seeks to explore, in particular the motivation for, and the relevance and value of digital commentary within recommender systems in the use and engagement with OER.
One of the major challenges to OER adoption and use is the concept of quality and trust. Atenas and Havemann (2014) have identified ten indicators for quality assurance which included peer review and social media tools for sharing resources. However current research by the author has perceived that there appears to be a ‘digital disconnect’ between these highly valued indicators, ROER that employ recommender system technology and user engagement /activity with these tools.
This interactive presentation will share some of the initial work undertaken as part of the doctoral research and actively engage with delegates to encourage them to consider and debate their own relationship with recommender systems; to what extent does the existence of recommender systems in ROER support and influence their own OER selection and use?
Atenas, J. and Havemann, L. (2014) Questions of quality in repositories of open educational resources: a literature review. Research in Learning Technology [online]. v.22, (July) Available from: http://www.researchinlearningtechnology.net/index.php/rlt/article/view/20889 [Accessed 11 November 2015]
California State University (2015) Merlot II Available from: https://www.merlot.org/merlot/index.htm [Accessed 11 November 2015)
Campbell, L.M. and Barker, P. (2013) Activity Data and Paradata [online]. Centre for Educational Technology, Interoperability and Standards. Available from http://publications.cetis.org.uk/2013/808 [Accessed 11 November 2015]
Manouselis, N., Drachsler, H. and Verbert, K. (2012) Recommender systems for learning: Springer briefs in Electrical and Computer Engineering. London: Springer
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Martin Hawksey posted an update in the session Reading between the lines: researching the impact of recommender systems in the engagement with and 8 years ago
Unfortunately this session will no longer be presented
