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![Page 1: Abstractions of Reality: Learning Analytics Kate Bridgeman Dr Anji Gardiner Patrick Lynch With special thanks to Ben Scoble at Staffordshire University.](https://reader035.fdocuments.in/reader035/viewer/2022062404/5515fbac55034694308b4979/html5/thumbnails/1.jpg)
Abstractions of Reality:
Learning AnalyticsKate Bridgeman
Dr Anji Gardiner
Patrick Lynch
With special thanks to Ben Scoble at Staffordshire University
![Page 2: Abstractions of Reality: Learning Analytics Kate Bridgeman Dr Anji Gardiner Patrick Lynch With special thanks to Ben Scoble at Staffordshire University.](https://reader035.fdocuments.in/reader035/viewer/2022062404/5515fbac55034694308b4979/html5/thumbnails/2.jpg)
Learning analytics?
Analytics is the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated
future data.
Cooper, A. (2012). CETIS Analytics Series Volume 1, No 5: What is Analytics? Definition and Essential Characteristics. The University of Bolton. [online] Available at < http://publications.cetis.ac.uk/wp-content/uploads/2012/10/What-is-Analytics-Vol1-No-5.pdf > [Accessed 7 January 2013].
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Introduction to Learning Analytics
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UNESCO IITE(2012) Policy Brief: Learning Analytics.
Meaning?
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Context
• Study skills module, FHSC; blended delivery
• Exploration eBridge “Site stats” tool
• Interesting activity noted
• Raft data
• How use to inform student & staff?
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Currently.....
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http://officeimg.vo.msecnd.net/en-us/images/MH900422121.jpg
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Key elements of our experimental research
• Identify important activities students should engage with
• Extract activity data identified via eBridge stats tool
• What would help staff work with & use data
• What would help students work with & use data
• How provide meaningful representation
• Making sense of the meaning
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Method
• eBridge VLE, statistics tool
• Assumptions made that data is correct & some basic cross referencing carried out
• Sample of 10 students as large module and lots of data
• Measurable periodso Course broken down into 6 workshop sessions
with
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Collate raw data from eBridge statistics
Workshop 1: 30/11/11 to 13/11/11
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Coding the data
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Behaviour /colour code & Colour map
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The complete module – six workshops
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Conclusion• We have confidence in the eBridge statistical data
• We can analyse the data mechanically and present it in an abstract form
• The abstraction has meaning for the tutor without all of the background data
– but, we have created layers of abstraction that can be mined
• This analysis provides the opportunity to have intelligence which cannot be obtained from classroom setting
• Analytics leads to new ways of thinking and opens up new possibilities for action
• Some of the preventative elements for staff in engaging with this kind of exploration could be removed through this work
• Even little data is big! There are over 555,000 events in the course log from 160 students
Change the way you think about Hull | 7 October 2009 | 14
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Next steps• More data – modules
• Transferability
• Live data
• Sharing data with students
• Further automation, parameterisation
• Linking with other systems, e.g. AIS
• Instructional design can inform the intelligence that you gather and vice-versa
• Further qualitative analysis – discourse analysis
• Prediction analytics
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Kate Bridgeman: [email protected]
Dr Anji Gardiner: [email protected]
Patrick Lynch: [email protected]
Thank you for listening
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Questions
• Transferability?
• Infrastructure?
• Others?
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