Introduction to the learning analytics and some learning analytics techniques anyone can use.

1. Learning Analytics
ways of visualising educational data
Andrew Deacon
Centre for Educational Technology
University of Cape Town
CET Seminar 2012

2. Outline
• Understandings of learning analytics
• Data landscape of learning
• Toolsets and reproducible research
• Looking for trends in large datasets
• Visualizing data beyond dashboards
• Future scenarios

3. ‘Analytics’ as a search-term
Google Trends

4. Learning Analytics
The measurement, collection, analysis
and reporting of data about learners
and their contexts, for purposes of
understanding and optimising learning
and the environments in which it
occurs.
Learning Analytics 2011 Conference, https://tekri.athabascau.ca/analytics

5. Age of Big Data
Source: The Economist

6. What foundations fund
• Bill & Melinda Gates Foundation
http://www.gatesfoundation.org
• Kresge Foundation
http://www.kresge.org
• Michael & Susan Dell Foundation
http://www.msdf.org
And what gets little support

7. Educational data landscape
Institutional Individual
Institutional data Social media Personal Learning
& learning environments & social learning Environments (PLE)
• ERP Systems
• Historical performance data
• Learning management system data
• Libraries
• School application data
• Turnitin Reports
• Demographics
Data is Data is Data is
• Accessible • Restricted • Almost unattainable
• Can identify individuals • Difficult to link to • Difficult to link to individuals
individuals

8. Reproducible Research
• Conducting analyses such that:
– code transforms raw data and meta-data
– code runs analyses on this processed data
– code incorporates this analyses into a report
• Sharing allows other to:
– confirm the correctness of the analyses
– do analyses not reported by original researchers

9. Institutional

10. Purdue University's Course Signals
• Early warning signs
provides intervention to
students who may not
be performing well
• Marks from course
• Time on tasks
• Past performance Source:
http://www.itap.purdue.edu/learning/tools/signals

12. Students’ use of Vula in a course
Submission of
assignments
Polling of
students
Site visits
Content
accessed
Chat room
activity
Sectioning
of students

13. Sociogram of a discussion forum
Dawson (2010)

14. Words in chats used by failing students

15. Words used by Lecturers vs Students
Marks;
thanks;
‘Weiten’ – test;
textbook Tut;
author guys
Week;
pages
Used more by Used more by
Lecturers/tutors Students

16. Beyond the Institution
Social Media / PLEs

17. Big breakthroughs happen
when what is suddenly
possible meets what is
desperately necessary.
Thomas Friedman
New York Times, 15 May 2012

18. High profile MOOCs

19. Coursera free online course

20. Experiment:
Concept Mapping or Retrieval Practice
J D Karpicke, J R Blunt Science 2011;331:772-775
Published by AAAS

21. If our aim is to understand people’s
behaviour rather than simply to record
it, we want to know about primary
groups, neighbourhoods, organizations,
social circles, and communities; about
interaction, communication, role
expectations, and social control.
Allen Barton, 1968, cited in Freeman (2004)
Source: CC BY-SA 3.0

22. UCT and social media
• Prominent links to:
– Facebook
– Flickr
– LinkedIn
– YouTube

23. Twitter: UCT chatter
• Six months of data (April – Sept 2011)
• Tweets including a UCT hashtag or text
#UCT, #Ikeys, University of Cape Town, …
• Attributes; how tweets are amplified
• Just over 5,000 tweets
Cannot capture every tweet on the topic
And some data cleaning required

24. Twitter: apps & locations
Blackberry Twitter Ubersocial Others
17% Cell phones:
27% Blackberry
Smartphone
geo-location
20%
36%
Cell phones

25. Twitter: viral #UCT
6 months of tweets Varsity Cup
final
Helicopter
crash

26. Twitter: tweeter relationships
Small number of
frequent tweeters
1. Drama student
(162)
2. UCT Radio
(132)
3. Science student
(84)

27. Flickr: helicopter crash at UCT
Ian Barbour - http://www.flickr.com/people/barbourians/

28. Twitter: helicopter crash at UCT
• Crash or
hard-landing?
• Media outlets getting
re-tweeted
• Peak: 140 in 5 min
2 hours
after the
event

29. Facebook: all friend relationships
Paul Butler http://www.facebook.com/notes/facebook-engineering/visualizing-friendships/469716398919

30. 1st-year course
combinations HS
HUM
COM
SCI
EBE

31. Correlation and causation
• Correlation does not imply causation
– Covariation is a necessary but not a sufficient
condition for causality
– Correlation is not causation
(but could be a hint)

32. Future scenarios
• Research requiring
– More detailed institutional data sets
– Analysis including social media & PLE data
– Modelling and predicting success
– Reproducible research
– Ethical considerations
• Visualisations and multivariant analysis
– Deepening understandings
– Making information more accessible

33. Tools used
Tool Example
TAGS

34. Software references
• Gephi – network analysis, data collection
• NodeXL – network analysis, data collection
• TAGS – data collection (Google Doc)
• Word cloud – R package (wordcloud)
• Geo-location map – R package (RgoogleMaps)
• Excel – spreadsheet, charts
• SPSS – statistical analysis, graphs

36. Literature references
• Baker, S.J.D., Yacef, K. (2009) The State of Educational Data Mining in 2009:
A Review and Future Visions:
http://www.educationaldatamining.org/JEDM/images/articles/vol1/issue1
/JEDMVol1Issue1_BakerYacef.pdf
• Freeman, C. (2004) The Development of Social Network Analysis: A Study
in the Sociology of Science. Empirical Press: Vancouver, BC Canada.
• Fritz, J. (2011) Learning Analytics. Presentation prepared for Learning and
Knowledge Analytics course 2011
(LAK11). http://www.slideshare.net/BCcampus/learning-analytics-fritz
• Kirschner, P.A., Karpinski, A.C. (2010) Facebook and academic
performance. Computers in Human Behavior, 26: 1237-1245.
• Dawson, S. 2010. ‘Seeing’ the learning community: An exploration of the
development of a resource for monitoring online student networking.
British Journal of Educational Technology, 41(5), 736-752.

37. Draws from a presentation at SAAIR 2011:
“Visualising activity in learning networks using
open data and educational analytics”
by Andrew Deacon and Michael Paskevicius