This document discusses analyzing social networks in learning communities. It outlines the data landscape in educational institutions, including institutional data, personal learning environments, and social media. It provides examples of visualizing and interpreting relationships in social networks using tools like Twitter, Facebook, LinkedIn, and course data. Finally, it discusses future scenarios for using social networks in educational research and visualization.

1. Social Networks
Analysing relationships in learning communities
Andrew Deacon
Centre for Educational Technology
University of Cape Town
EDN6099F – 20 March 2013

2. Outline
• Measures of success
• Looking at social networks
• Data landscape in learning organizations
• Interpreting relationships in social networks
• Identifying trends in learning environments
• Imagining future scenarios

3. Three eras of social research
1. Age of Quételet –
collect data on simple
& important questions
2. Classical period –
inference theory to get
the most information
from a little data
3. Present day big data –
deluge of data and
questions

4. Predicting success
MAT – School mathematics test
(university admissions)
Chemistry – 1st year university exam
(first-year success)

5. Predicting success
Top
quarter
Student MAT of both Chemistry
Student 1 66 42
Student 2 90 92
Student 3 74 51
Student 4 63 58
Student 5 73 69
Student 6 73 68
Student 7 88 90
Student 8 81 77
Student 9 69 61
Student 10 64 66
Student 11 81 75
Student 12 92 88

6. Predicting success

7. Predicting success

8. Predicting success

9. 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

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

11. Beyond the institution context
Social Media / PLEs / CoP

12. UCT and social media
Prominent links to:
– Facebook
– Flickr
– LinkedIn
– Twitter

13. Twitter: UCT chatter
• Looked at 6 months of data
April – Sept 2011
• Selected tweets with a UCT hashtag or text
#UCT, #Ikeys, University of Cape Town, …
• Attributes
tweet amplification, app used, location
• Dataset
Just over 5,000 tweets

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

15. Twitter: tweeter relationships
Frequent tweeters:
1. Drama student
(162)
2. UCT Radio
(132)
3. Science student
(84)

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

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

18. Twitter: helicopter crash at UCT
• Peak of 140 tweets
in 5 minutes
• Media organisations
tweets get re-tweeted
• Crash or hard-landing?
2 hours
after the
event

19. Ingredient Networks
for Recipe Recommendations
Lada Adamic http://www.foodandtechconnect.com/siteold/2011/11/28/mining-allrecipes-coms-ingredient-networks-for-recipe-recommendations/

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

21. LinkedIn Maps
http://inmaps.linkedinlabs.com

22. Within the institutional contexts
Course data / LMS

23. 1st-year course
combinations HS
HUM
COM
SCI
EBE

24. Maths and Maths Literacy
UCT Humanities students course combinations
26% to 50%
Maths Lit
More than 50%
Maths Lit
No
Maths Lit
1% to 25%
Maths Lit

25. 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

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

28. Sociogram of a discussion forum
Dawson (2010)

29. Words in chats used by failing students

30. 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

31. Effective visualisations
The success of a visualization is
based on deep knowledge and
care about the substance, and the
quality, relevance and integrity of
the content.
Tufte (1981)

32. 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)

33. Future scenarios
• Social networks in educational research:
– Understanding social media & PLEs for learning
– Institutional data from a student perspective
– Connectionist theories of learning
– Ethical considerations
• Visualisations of social networks:
– Good open source software available
– Observation and analysis many outcome variables

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

35. Literature references
• 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.
• Deacon, A., Paskeviciusat, M. (2011) Visualising activity in learning
networks using open data and educational analytics. Southern African
Association for Institutional Research Forum, Cape Town.
• 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
• Hansen, D., Shneiderman, B., Smith, M.A. (2011) Analyzing Social Media
Networks with NodeXL: Insights from a Connected World, Morgan
Kaufmann Publishers, San Francisco, CA.
• Tufte, E. (1981) The visual display of quantitative information. Cheshire,
Conn.: Graphics Press.

36. Font references
• FatFonts by Miguel Nacenta, Uta Hinrichs and
Sheelagh Carpendale. The area of each number
is proportional to its value http://fatfonts.org
Source: http://fatfonts.org