Social networks and learning -- examples and highlights of studies on social networks and learning communities. Haythornthwaite, C. (June 30, 2014). Network Madness: A node, a relation, a network. Invited presentation, Learning Analytics Summer Institute 2014 - Public Event, Harvard University, Boston MA (one of four invited speakers). Organizer Garron Hillaire. http://www.meetup.com/Learning-Analytics-Boston/events/187455892/

1. Network Madness
Caroline Haythornthwaite,The iSchool @ The University of British Columbia
Presented at the Learning Analytics Summer Institute, 2014, Boston, MA
A node, a relation, a network

2. Social Network Perspective
— Actors people, groups, organizations
— Tied by one or more relations
◦ Sometimes strongly tied
◦ Sometimes weakly tied
— Revealed as networks
— Analyzed and displayed as graphs

3. Network Questions
— Who learns from whom?
◦ Who talks to, gives help to,
collaborates with whom?
— What do they learn from each
other?
— Which media support which
kinds of learning?
— What outcomes do
these relations build?
◦ Access to resources
◦ Trust, mobility, equity, etc.
— What benefit accrues to the
network?
◦ social capital, shared
knowledge, shared resources
— How do resources flow in the
network
abc123@321efg
abc123@321efg
abc123@321efg
Twitter – node size = accounts that are frequently mentioned,
replied to or whose tweets are frequently retweeted
abc123@321efg
abc123@321efg
abc123@321efg

4. Strong and Weak Ties
StrongTies …
— Maintain more relations
— Have more frequent
interaction
— Include intimacy and self-disclosure
— Use more media
— Have higher reciprocity in
exchanges
Source of
• Freely given resources
• Feel obligation to share
! Questions
• How do you build strong learning ties,
online and through computer media?
• How do you motivate sharing in crowd-
and community-based initiatives?
• How do you build learning
communities?

5. Strong and Weak Ties
WeakTies …
— Engage in fewer, less intimate
exchanges
— Have more instrumental
exchanges
— Share fewer types of
information and support
— Use fewer media
Source of…
• New information, new resources
• Have little or no obligation to share
à Questions
• How do you bring peripheral actors
into the learning community?
• What is the right mix of tie strength to
sustain innovation and commitment?

6. Social Networks and Learning
Who to whom
— Who talks to, learns from, collaborates with
whom?
— What are the attributes of these actors?
What
— What do pairs talk about, do together?
— What does the network talk about, do
together?
Structure
— How does information circulate in a network?
— Who are the key actors who facilitate or
hinder information movement?
— Where is ‘expertise’ located?
Outcomes
— What identifiable relations, actor interactions,
information exchange binds the network?
— What social outcomes to these relations
build? trust, resources/services, mobility,
equality, opportunity, common knowledge
— What benefit resides in the network? -- social
capital
— Who talks to whom, about what, and via
which media?
— Who learns from whom?
— What relations constitute a learning tie? And/
or sustain a learning network?
— Which media support which kinds of
ties and relations
◦ How are ties, relations, networks maintained,
online and off, in the service of learning?
— What network structures emerge in the
service of learning?
— What impact do different strategies,
pedagogies, teaching and learning
practices have on network relations, ties
and structures?
◦ How do emergent structures align with
pedagogical, collaborative, cooperative – or even
isolationist – expectations and intentions?
◦ Whate learning outcomes result for individuals,
cliques, networks?
— What can we learn from network analyses
that inform design and design practice for
learning

7. Networks Are More Than Pictures
Networks show
— density
— actor centrality
— centralization
— cliques
— stars
— brokers
— isolates
— cliques
— structural holes
— path lengths
Network outcomes
— Resource flow
◦ inclusion and
exclusion
◦ early and late
access to
information
— Roles
◦ stars, gatekeepers,
entrepreneurs,
brokers,
translators
◦ information
suppliers, help
givers, social
support givers
— Social structures
◦ Social capital,
resilience
Collaborating on
class work

8. Who learns from whom, about what,
and via what means?
— Roles and Positions
◦ Technological guru, learner-
leaders, translators,
◦ Question askers and answerers
◦ Network stars and brokers
— Relations
◦ Information exchange, social
support, help giving
— Media
◦ Public and private
◦ Threaded (twitter) or
composite (wiki),
◦ Single (lecture hall) or multiple
(online/offline in various forms)
— Structures
◦ What structures emerge in the
in open learning environments?
◦ What is a ‘good’ structure?
◦ What impact do different
strategies, pedagogies, teaching
and learning practices have on
network relations, ties and
structures?
— Social Capital
◦ What benefits accrue to the
network?
— Design
◦ What can we learn from
network analyses that inform
design and design practice for
learning?

9. Structure Tells Tales …
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Network Evolution:
Email network over time Hidden Structures: External links; Internal core
Media Use: Chat, Discussion Board, Email
Media multiplexity
Classes and media form
latent tie structures on
which weak ties can build
into stronger ties

10. Discovery
— Who
◦ How do we identify
actors and their roles in
learning networks
— What
◦ What relations and ties
do people maintain? What
do they learn from each
other?
— Structure
◦ What network
connections are revealed
through learning ties?
— Moving toward
automated discovery

11. Node and tie discovery
Previous post is by Gabriel, Sam replies:
‘Nick,Ann, Gina, Gabriel:
I apologize for not backing this up with a good source,
but I know from reading about this topic that libraries…’
Previous posts by Gabriel, Sam, Gina, and Eva, then:
‘Gina, I owe you a cookie.This is exactly what I wanted to know.
I was already planning on taking 302 next semester,
and now I have something to look forward to!’
Post by Fred:
‘I wonder if that could be why other libraries
around the world have resisted changing –
it's too much work, and as Dan pointed out, too expensive.’
Ex.1
Ex.2
Ex.3
Gruzd,A. & Haythornthwaite, C. (2008). Automated discovery and analysis of social networks from threaded discussions. International
Sunbelt Social Network conference, Jan. 22-27, St. Pete’s Beach, Florida. [http://hdl.handle.net/2142/11528]

12. Nodes and ties in Twitter
— Who mentions
and/or replies to
whom
— Reveals a single
large component
with a moderate
periphery of
observers
Automated data collection: Who mentioned or
replied to whom, twitter network. Health care
learning community, #hcsmca (H&G, 2013)

13. Prestige and Influence
Green = social media health content providers
Blue = communicators, health related
Pink = advocacy
• Who is mentioned,
replied to most has
the greatest prestige
(In-degree) = node size
here
• Or, can see who
mentions or replies
to others most = the
greatest influence
(out-degree]

14. What do people learn from each other?
— Learning Relations
◦ What did you learn from the 5-8
others with whom you
communicate most frequently?
◦ Questionnaires and content
analyses
0
10
20
30
40
50
60
70
Fact/Field
Process
M
ethod
R
esearch
Technology
G
enerate
Socialization
N
etw
orkingA
dm
inistration
Types of Learning: ReceivedInterdisciplinary Teams
Science Teachers
Distribution*of*‘learn*from’*relations*
Relation) 256) 100%)
Teaching*techniques*(T)* 173* 68*
Science*Content*(C)* 72* 28*
Classroom*Management*(M)* 32* 13*
External*Matters*(E)* 27* 11*
Administrative*functions*(A)* 17* 7*
None* 9* 4*
Relational multiplexity
in learning ties

15. Entrepreneurial Leadership in STEM : http://enlist.illinois.edu/
NB: caveat about data coverage: dataset covers only a limited number of schools and respondents, and data
collection from first time participants occurred at two time periods a year apart (one cohort in summer 2009,
two in summer 2010)
Revealing structures
Connections
across schools
build by learning
relationships:
I learn from / they
learn from me
about science
teaching

16. Learning from Networks
Using networks to interpret, analyze and design for community
A professional
development
network for a
school
(de Laat, 2010)
Shown back to
participants so they
can see how their
networks are
connected

17. More …
Look at
change
over
time
See how each medium
plays a role in
maintaining the
community: chat,
discussion,
email
Take a network
perspective on
motivating
contribution in
crowds and
communities
Explore these SN tools for analysis
of learning environments:
Netlytic https://netlytic.org/
(Anatoliy Gruzd)
SNAPP http://www.snappvis.org
(Shane Dawson)
Your Questions and
Network Studies Go Here

18. Further reading: short list -- see also http://haythorn.wordpress.com/
— Gruzd, A. & Haythornthwaite, C. (2013). Enabling community through social
media. Journal of Medical Internet Research. 2013;15(10):e248.
http://www.jmir.org/2013/10/e248/.
— Haythornthwaite, C. & De Laat, M. (2011). Social network informed design
for learning with educational technology. In A.D. Olofsson & J. O. Lindberg,
(Eds.). Informed Design of EducationalTechnologies in Higher Education (pp.
352-374). IGI Global.
— Haythornthwaite, C. (2008). Learning relations and networks in web-based
communities. International Journal ofWeb Based Communities, 4(2), 140-158.
http://www.inderscience.com/info/filter.php?aid=17669.
— Haythornthwaite, C. (2007). Social networks and online community. In A.
Joinson, K. McKenna, U. Reips & T. Postmes (Eds.), Oxford Handbook of Internet
Psychology (pp. 121-136). Oxford, UK: Oxford University Press.

19. Learning networks, learning analytics
— Gruzd, A. & Haythornthwaite, C. (2013). Enabling community through social media. Journal of Medical Internet Research.
2013;15(10):e248. http://www.jmir.org/2013/10/e248/.
— Haythornthwaite, C., De Laat, M. & Dawson, S. (Eds.) (2013). Learning analytics. American Behavioral Scientist, 57(10), whole issue.
— Haythornthwaite, C. & De Laat, M. (2011). Social network informed design for learning with educational technology. In A.D.
Olofsson & J. O. Lindberg, (Eds.). Informed Design of EducationalTechnologies in Higher Education (pp. 352-374). IGI Global.
— Haythornthwaite, C. (2008). Learning relations and networks in web-based communities. International Journal ofWeb Based
Communities, 4(2), 140-158. Selected as one of top 10 papers in IJWBC in its first 10 years and made open access: http://
www.inderscience.com/info/filter.php?aid=17669.
Discovering relations
— Haythornthwaite, C., Gao,W. & Abd-El-Khalick, F. (2014). Networks of change: Learning from peers about science teaching.
Proceedings of the 47th Hawaii International Conference on System Sciences, Big Island, HI. Los Alamitos, CA: IEEE.
— Haythornthwaite, C. (2006). Learning and knowledge exchanges in interdisciplinary collaborations. Journal of the American Society
for Information Science andTechnology, 57(8), 1079-1092.
— Haythornthwaite, C. (2001). Exploring multiplexity: Social network structures in a computer-supported distance learning class.
The Information Society, 17(3), 211-226.
Structures: latent ties, internet connectivity, crowds and community
— Budhathoki, N. & Haythornthwaite, C. (2013). Motivation for open collaboration: Crowd and community models and the case
of OpenStreetMap. American Behavioral Scientist, 57(5), 548 - 575.
— Haythornthwaite, C. (Jan. 2009). Crowds and communities: Light and heavyweight models of peer production. Proceedings of the
42nd Hawaii International Conference on System Sciences. Los Alamitos, CA: IEEE. [http://hdl.handle.net/2142/9457]
— Haythornthwaite, C. (2007). Social networks and online community. In A. Joinson, K. McKenna, U. Reips & T. Postmes (Eds.),
Oxford Handbook of Internet Psychology (pp. 121-136). Oxford, UK: Oxford University Press.
— Haythornthwaite,C.(2005). Social networks and Internet connectivity effects. Information, Communication & Society, 8(2),125-147.
— Haythornthwaite, C. (2002). Strong, weak and latent ties and the impact of new media. The Information Society, 18(5), 385 – 401.
Further reading: long list -- see also http://haythorn.wordpress.com/