Data analytics and learning technologies: what is possible and can it inform practice?

Linsey, Tim and van der Sluis, Hendrik (2012) Data analytics and learning technologies: what is possible and can it inform practice? In: Educational Research Forum; 15 Jun 2012, Kingston upon Thames, U.K.. (Unpublished)

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Abstract

VLEs and other technologies commonly used for enhancing learning and teaching collect a variety of data concerning for example, general system use, individual participation and aggregated information, use of particular tools etc. Some of this data is made available to users using inbuilt reporting tools, but in many cases this data is only a subset of what is available. This additional data can, in many cases, be accessed and mined with specialist support and tools. Data mining is a broad term covering many types of analysis, methods and tools for exploring large data sets to identify, for example, patterns and relationships. These ‘learning analytics’ outputs, such as user patterns and diagnostics, will, according to Horizon (2011: 28), improve the ‘understanding of teaching and learning’. This may include a better understanding of how specific technologies are actually used as opposed to their expected use, and therefore informing learning and teaching practice. This presentation will report on ongoing work in the Academic Development Centre to explore these analytical approaches as applied to institutional supported learning technologies, and will present some initial findings. The presentation will conclude with a consideration on how these methods might contribute to further educational research at Kingston University.

Item Type: Conference or Workshop Item (Paper)
Event Title: Educational Research Forum
Organising Body: Higher Education Policy and Practice (HEPP) research network, Academic Development Centre
Uncontrolled Keywords: HEPP
Research Area: Education
Faculty, School or Research Centre: Services and Administration (until 2017)
Depositing User: Hendrik Van Der sluis
Date Deposited: 20 Mar 2013 13:35
Last Modified: 12 Jun 2013 08:56
URI: http://eprints.kingston.ac.uk/id/eprint/24182

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