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Transcript of Hay network madness lasi14.pptx
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
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
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
Strong and Weak Ties Strong Ties … � 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?
Strong and Weak Ties Weak Ties … � 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?
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
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
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?
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
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
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]
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)
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]
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
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Fact /
Fiel
d
Proce
ss
Method
Resea
rch
Tech
nology
Gener
ate
Socializ
atio
n
Network
ing
Admin
istra
tion
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
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
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
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
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 Educational Technologies in Higher Education (pp. 352-374). IGI Global.
� Haythornthwaite, C. (2008). Learning relations and networks in web-based communities. International Journal of Web 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.
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 Educational Technologies in Higher Education (pp. 352-374). IGI Global.
� Haythornthwaite, C. (2008). Learning relations and networks in web-based communities. International Journal of Web 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 and Technology, 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/