Abstractions of Reality:

Learning AnalyticsKate Bridgeman

Dr Anji Gardiner

Patrick Lynch

With special thanks to Ben Scoble at Staffordshire University

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

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

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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: Kate.bridgeman@hull.ac.uk

Dr Anji Gardiner: A.B.Gardiner@hull.ac.uk

Patrick Lynch: P.Lynch@hull.ac.uk

Thank you for listening

Questions

• Transferability?

• Infrastructure?

• Others?

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