This document summarizes a presentation on social network analytics in education and research. The presentation discusses how social networks are used in education, both inside and outside institutions. It explores how social network data could be used to show the value of networks, enhance their value, and improve their use. Examples are provided of social network analysis tools and metrics. The presentation cautions that not everything can or should be measured, and data can be misused. It advocates responding to the "age of data" by combining data and efforts, using visualization, and focusing on stories as well as numbers.

1. Thursday 23rd February 2012
JISC CETIS Conference 2012
Author: Amber Thomas
(c) HEFCE on behalf of JISC
www.jisc.ac.uk
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Social Network Analytics
Amber Thomas
JISC Programme Manager
http://slidesha.re/z44AqU

2. Thursday 23rd February 2012
JISC CETIS Conference 2012
Social Networks
in Education and Research
Social Network Analytics
in Education and Research
Examples

3. Who are the social networks in education?
Who is the social network?
• inside the institution:
o academics: as researchers
o academics: as teachers
o services:
libraries, IT, catering, accommodation, conferences
o students
• institutional management has different levels of control, each
has different motivations
• outside the institution:
o potential learners and informal learners
o the public/society
o journalists
o employers
Not everyone engages in the same way:
Users and non users, visitors and residents 3

4. A first look:
Social network use in education
wikis as collaboration
student as media producer
A message and a medium
practitioner peer to peer
amplification of teaching
Social media as speaking
(marketing) amplification of events
Social media as listening (user broadcasting platforms e.g youtube
feedback, market intelligence)
blogging as digital scholarship
Social media as exchange
(learning, scholarship, CRM?) blogging as services update
universities on facebook
twitter as alerts
twitter as exchange
4

5. The value of social networks in education
Lets give social networks the benefit of the doubt:
They are a good thing
They are a useful thing
Lets assume no-one is questioning that.
So ... social networks generate data. Can that data help to:
 Show the value of social networks?
 Enhance the value of social networks?
 Improve the way we use social networks?
What’s not to like?
5

6. Is social network analysis a trap?
6

7. Thursday 23rd February 2012
JISC CETIS Conference 2012
Social Networks
in Education and Research
Social Network Analytics
in Education and Research
Examples

8. Who’s agenda is this anyway?
comfort zone dirty words
OPEN ACCESS TO RESEARCH IMPACT
OPEN INNOVATION BRAND
OPEN EDU RESOURCES COST BENEFIT ANALYSIS
KNOWLEDGE SHARING METRICS
ACADEMIC AUTONOMY KPIs
PUBLIC GOOD BUSINESS CASES
LANGUAGE OF VALUES LANGUAGE OF THE MARKET
8

9. [Data capture is not [Analytics] is not Metrics]
DATA CAPTURE
The Data Exists Anyway
METRICS
(come later)
9

10. Demystifying the role of data in decision-making
DECISIONS
“Evidence-based decision making”
&
“Data-driven decision making”
DECISION-
METRICS EVIDENCE MAKER(s)
10

11. Demystifying the role of data in decision-making
the messy reality
“Evidence-informed RESOURCES
& data-informed DECISIONS
decision making”
VALUES
STRATEGIES
DECISION-
EVIDENCE MAKER(s)
METRICS
ETC !
IMPACT
MODELS STAKEHOLDER
VIEWS
11

12. Demystifying data in an age of data
Opportunity:
LANGUAGE OF THE MARKET
Using the techniques of the
LANGUAGE OF VALUES
market to support our values
in the age of data
APIs
Feeds
Web analytics
Data visualisation
Storytelling
12

13. Tactics for responding to the age of data
• deny it's happening
• leave the people in suits to
work it out
• pay lip service
• produce numbers
• produce stories
• deepen our listening
approaches
• improve our metrics
• broaden our impact model
• extend our impact timeframe
13

14. Thursday 23rd February 2012
JISC CETIS Conference 2012
Social Networks
in Education and Research
Social Network Analytics
in Education and Research
Examples

15. The age of data
In the age of data, data is everywhere
... even if its not the whole story
Decision-makers want numbers
... even if they don’t “understand” them
and
People like pictures

16. Decision makers want numbers
(even if they don’t understand them)
http://scaleofuniverse.com/

17. People Like Pictures

18. Principles for responding to the age of data
In the age of data, data is everywhere
Decision-makers want numbers
People like pictures
BUT
Not everything can be measured
Not everything should be measured
There are legal and ethical issues
Data can be mistaken, misunderstood and misused

19. A Lesson in Glorious Obfuscation
http://www.tubechop.com/watch/281562

20. Tactics for responding to the age of data
Reduce our fear of numbers
Be generous with data, not guarded
Increase our data literacy and our technical skills
Combine data, combine effort, work faster
Imagine we’re all talking to strangers
Every data it’s story, every story it’s data
Glanceability in an abundance of data: visual matters
20

21. Looking again:
Social network use in education
wikis as collaboration
student as media producer
A message and a medium
practitioner peer to peer
amplification of teaching
Social media as speaking
(marketing) amplification of events
Social media as listening (user broadcasting platforms e.g youtube
feedback, market intelligence)
blogging as digital scholarship
Social media as exchange
(learning, scholarship, CRM?) blogging as services update
universities on facebook
twitter as alerts
twitter as exchange
21

22. Examples
Aside from the Hirst and the Hawksey, please see also:
 Institutional use of social media eg Brian Kelly
http://ukwebfocus.wordpress.com/
 New models of impact for research eg ALT Metrics
http://altmetrics.org/manifesto/
 Learning analytics eg Siemens et al
http://en.wikipedia.org/wiki/Learning_analytics
 Text mining twitter eg Proctor, Manchester
http://www.guardian.co.uk/uk/2011/dec/07/twitter-riots-how-
news-spread

23. My SNA Examples
Who is the hashtag community?
Heart of #UKOER
http://zoom.it/6ucv5
By
Martin Hawksey
UKOER Viz Project

24. My SNA Examples
Are two people being listened to by the same people?
dkernohan & ambrouk
Twiangulate
Mutual Followers
Analysis
From
www.twiangulate.com
Following 205 people in common of 1050 total
tweeps followed

25. My SNA Examples
Are two people listening to the same people?
dkernohan & ambrouk
Twiangulate
Mutual Friends
Analysis
From
www.twiangulate.com
Followed by 244 people in common of 1336
total following tweeps

26. My SNA Examples
A Rather Cunning and Brave
and Possibly Foolish Experiment:
How much use/reach/impact does this presentation have?
http://slidesha.re/z44AqU
See topsy tracking links in the notes of this presentation
Pick your favourite:
personal impact = pimpact
vanity analytics = vanalytics
Tweet/use your favourite term online
And I’ll do something interesting/useful/vain with it

27. Thank You
Amber Thomas
Programme Manager
Digital Infrastructure Team
Twitter: @ambrouk
Skype: amber_thomas
Full Contact Details:
http://www.jisc.ac.uk/contactus/staff/amberthomas.aspx
My blog posts: http://bit.ly/zBQ2er