1. Abstractions of Reality: Learning Analytics
Kate Bridgeman
Dr Anji Gardiner
Patrick Lynch
With special thanks to Ben Scoble at Staffordshire University

2. 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 < content/uploads/2012/10/What-is-Analytics-Vol1-No-5.pdf > [Accessed 7 January 2013].
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3. Introduction to Learning Analytics
Meaning?
UNESCO IITE(2012) Policy Brief: Learning Analytics.
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5. 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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6. Currently.....
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7. 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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8. 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 periods
Course broken down into 6 workshop sessions with
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9. Collate raw data from eBridge statistics
Workshop 1: 30/11/11 to 13/11/11
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10. Coding the data
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11. Behaviour /colour code & Colour map
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12. The complete module – six workshops
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14. 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 students
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15. 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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17. Thank you for listening
Kate Bridgeman: Dr Anji Gardiner: Patrick Lynch:

18. Questions Transferability? Infrastructure? Others?
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