Abstractions of Reality: Learning Analytics Kate 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]. Abstractions of reality: Learning Analytics| 9 January 2013| 2

Introduction to Learning Analytics Meaning? UNESCO IITE(2012) Policy Brief: Learning Analytics. Abstractions of reality: Learning Analytics| 9 January 2013| 3

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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? Abstractions of reality: Learning Analytics| 9 January 2013| 5

Currently..... http://officeimg.vo.msecnd.net/en-us/images/MH900422121.jpg Abstractions of reality: Learning Analytics| 9 January 2013| 6

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 Abstractions of reality: Learning Analytics| 9 January 2013| 7

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 Abstractions of reality: Learning Analytics| 9 January 2013| 8

Collate raw data from eBridge statistics Workshop 1: 30/11/11 to 13/11/11 Abstractions of reality: Learning Analytics| 9 January 2013| 9

Coding the data Abstractions of reality: Learning Analytics| 9 January 2013| 10

Behaviour /colour code & Colour map Abstractions of reality: Learning Analytics| 9 January 2013| 11

The complete module – six workshops Abstractions of reality: Learning Analytics| 9 January 2013| 12

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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

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 Abstractions of reality: Learning Analytics| 9 January 2013| 15

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Thank you for listening Kate Bridgeman: Kate.bridgeman@hull.ac.uk Dr Anji Gardiner: A.B.Gardiner@hull.ac.uk Patrick Lynch: P.Lynch@hull.ac.uk

Questions Transferability? Infrastructure? Others? Abstractions of reality: Learning Analytics| 9 January 2013| 18