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

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Twitter – node size = accounts that are frequently mentioned, replied to or whose tweets are frequently retweeted

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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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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/