Delivered October13, 2011 in Cape Town South Africa at the 2011 Southern African Association for Institutional Research forum Abstract As more student academic activities involve both institutional and social networks, educational analysts are needing to investigate ways in which this data can be collected and interpreted to enhance learning experiences. Data recorded as students explore personal learning environments is most often not accessible or incomplete. Here we explore some of the approaches that exist to use these social networking platforms along with information from the learning management system and academic records. Combining and analysing this data has allowed us to create a number of interesting visualizations exposing patterns which would have been impossible to glean from looking at the data alone. In an age of data abundance we reflect on using some of these new measures in relation to improving learning design, increasing academic responsiveness and enhanced student experiences.

1. Visualising activity in learning networks
using open data and educational
analytics
Andrew Deacon & Michael Paskevicius
Centre for Educational Technology, University of Cape Town
Southern African Association for Institutional Research
(SAAIR) Forum 2011

2. Centre for Educational Technology within the
Centre for Higher Education Development
– Michael Paskevicius (Learning Technologist)
• Interested in social media and open education
• Previously MIO at Polytechnic of Namibia
– Andrew Deacon (Learning Designer)
• Experienced learning designer
• Significant experience analysing assessment

3. Agenda
• Definition of educational analytics
• Explore the data landscape of institutional
learning environments, personal learning
environments and social media
• Learning analytics – approaches & challenges
at the University of Cape Town (Michael)
• Visualizing complex data – beyond univariant
dashboards (Andrew)
• Available toolsets and concluding thoughts

4. An age of data
• Massive increase in data storage
capability
• What about data collected within
learning environments?
Source: The Economist
Source: Telegraph Source: Deloitte Consulting

5. Educational 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 site: https://tekri.athabascau.ca/analytics)
• Exploring the unique types of data that come from educational
settings, and using those methods to better understand students,
and the settings which they learn in. (Baker & Yacef, 2009)
• Academic analytics can be used to profile and even predict
students who may be at risk, by analysing demographic and
performance data of former students. (Fritz, 2011)

7. Educational analytics data landscape
Social media
Institutional learning Personal learning
The social web
environments environments (PLE)
• ERP Systems
• Historical performance data
• Learning management system data
• Libraries
• School application data
• Turnitin Reports
• Demographics
Attributes Attributes Attributes
• Owned data • External data • External data
• Accessible • Mostly difficult to obtain if at all • Mostly difficult to obtain if at all
• Found in various databases • Difficult to connect to • Difficult to connect to
institutional data institutional data
• Perhaps not academic at all

8. 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, C. (2004)

9. Tools used
Tool name Example
Twitteralytics

10. Data sources
Web and activity log scraping
• How do people connect with each other in collaborative
academic environments?
• What types of interaction occur in a forum or chat room
discussion?
Social network analysis
Source: CC BY-SA 3.0 • What are people saying about our university in social
networks?
• How are students related within social networks?
Extract method:
• Query select data via API or script (Python, PHP, screen-
scraping programs)
• Group by hashtags, groups, users, topics, keywords
• Often requires addition of semantic understanding (and
associated documentation)

11. Institutional Learning
Environments

12. Starting point: UCT Learning
Management System

13. How and when do students use the
learning management system?
Submission of
assignments
Polling of
students
Site visits
Content
accessed
Chat room
activity
Sectioning
of students

14. Does student LMS activity correlate to course grade?

15. How do students and academics engage in a
Academics course chat room?
and support
staff
Days in
which chat
occurred
Chat messages
linked to day
of occurrence
Students

16. What do students who drop the
course chat mostly about?

17. How do students engage with
academics in a chat room?
Said more by Said more by
educators students

18. PLEs / Social Media

19. Exploratory data analysis
• Getting actual social media data
(vs surveys / aggregated data)
• Usage and trends
Confirm what happened
• Relationships
Explain how things are connected
• Comparisons
Serendipity as new questions arise

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

21. Twitter: student survey
Would use on my cell phone Yes
SMS 99%
Webmail 94%
Facebook 92%
Wikipedia 90%
Library journals 85%
Flickr, YouTube 74%
Google Docs 63%
Skype 61%
Twitter 26%
Vula student survey, 2010 data set

22. Twitter: UCT chatter
• Six months of data (April – Sept 2011)
• Tweets including a UCT hashtag
#UCT, #Ikeys, …
• Attributes; how tweets are amplified
• Just over 5,000 tweets
• Cannot capture everything referring to something
• Clean dataset to exclude other uses of hashtags

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

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

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

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

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

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

29. UCT: first-year courses
Psychology and Economics
courses have students registered
for the largest number of other course
(node size is the number of edges)

30. Data acquisition & preparation
• Social media data challenges
– Tools and data APIs changing
– Being commercialised (and throttled)
– Data cleaning required

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

32. Conclusions
• Exploring emerging data sources
– Combined institutional data sets
– Acknowledge Personal Learning Environments
– Highly fragmented social media data
– Collectively enrich existing information
• Visualisations and multivariant analysis
– New exploratory tools
– Making information more accessible

33. 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.

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