DCLA14_Haythornthwaite_Absar_Paulin

Words, Learning and Networks

Caroline Haythornthwaite Rafa Absar

Drew Paulin The iSchool @ UBC

University of British Columbia

Discourse Analytics Workshop LAK 14, 2014

Social Media and Learning �  Social Science and Humanities Research Grant (SSHRC) ◦  PIs Anatoliy Gruzd & Caroline Haythornthwaite, with George Siemens

++ Drew Paulin, Rafa Absar, Mick Huggett �  Primary purpose: ◦  To determine and evaluate measures that help educators manage their use

of social media for teaching and learning through the use of automated analysis of social media texts and networks

�  Examine facets such as ◦  common patterns of exchange ◦  development of shared language and understanding ◦  emergence of roles and positions

�  Primary approach ◦  Automated analysis of social media texts and networks ◦  Who talks to whom about what via and which (social) media?

�  Research goal is to discover ◦  What forms of social connection – conversational structures of

communication between people in a network – reveal learning, learning practices, learning roles, etc.

Social Networks Social network building blocks: Actors (nodes) Relations (lines) Network (graph)

Networks & Discourse �  Discourse / Conversation / Communication ◦  Entails using language, often in a symbolic and

prescribed way, that signals relations between objects, subjects, etc. – i.e., a network!

�  Social Networks ◦  Describe relations between actors that signal social

constructions such as cliques, groups, communities �  Discourse communities, epistemic communities, learning

communities

�  Relations can be determined from text ◦  The question for use is ‘What text signifies learning

relations?’

Learning and Networks �  Psychological basis of networks

�  Safety: leading to affiliation, group belonging -- embeddedness – strong ties �  ‘Effectance’: drive for autonomy, exploration, individuation – arm’s length –

weak ties (Kadushin; Uzzi; Granovetter)

�  Network outcomes ◦  Community

�  Reduced individual social load (Burt); generalized reciprocity; border/gate-keeping

Ø  Learning communities/Communities of Inquiry; Knowledge/ Epistemic communities

◦  Social Capital �  Resources held in the network �  Knowledge, expertise, physical and social support, companionship, trust, reserve

resources (Lin; Wellman)

�  Social structure affects outcomes �  Flow, quality and reach of information; Reward and punishment; Trust that

others will do the ‘right thing’ (Granovetter)

Learning Networks �  Learning as acquisition of knowledge, marked by

a transformation of the individual �  Information, knowledge, and learning networks

�  Social Learning as learning with, by and through networks

�  Accomplished through transfer of information, knowledge dissemination, discussio - trying out ideas on others

�  Transformation evident as adoption or development of common practices

�  Cultural, disciplinary, group language, discourse, genres, modes of communication, methodological approaches �  (Miller, 1984; DeSanctis & Poole, 1994; Haythornthwaite, 2006,

2013)

Learning from a Network Perspective

�  Learning can be a relation that connects people

�  Learning can be the characterization of the tie ◦  based on multiple,

contextually determined relations

�  Learning relations can be taken as input for design ◦  e.g., when addressing

differences between online and offline learning

�  Learning can be a characterization of the outcome of relations ◦  e.g., when a group becomes

a learning community

�  Learning as the network outcome of relations ◦  e.g., the social or learning

capital of the network

�  Learning as contact with ambient influence ◦  e.g., informal and

ubiquitous learning

Who learns what from whom

Learning

Networks

Words

�  What exchanges support a learning tie?

�  What relations and ties support a learning community?

�  What can we ‘see’ in the texts of learners? ◦  Online conversations, but also essay/

exam texts; images; videos; multimodal texts

◦  Across media: discussion, blogs, twitter �  Social networks of ◦  Learning groups: Actors in a learning

community ◦  Knowledge base: Topics in a knowledge

domain ◦  Bibliometric base: Stars in the citation

universe

Words, Learning and Networks �  Using text analysis to identify the building

blocks of networks ◦  Actors/nodes, relations, ties

�  Single mode ◦  Using text analysis to discover actors and relations

�  Two mode ◦  Actors x Text ‘events’ à ‘actor x actor’ AND ‘text x text’ networks

Use text analysis to distinguish: Actors in the network ◦  Who is in the network

Actor relations ◦  What text(s) tie actors in the

network?

◦  What relations do these identify?

◦  2-mode: What actors are tied because of common text use?

Actor ties ◦  Who talks to whom about what?

◦  Who is tied to whom by the identified relation(s)

◦  What constitutes weak to strong tie configurations for these actors (frequency, intimacy of relational /text content)

Social networks ◦  What configurations of actors tied by

text defines the network?

Text in the network ◦  What topics/phrases/keywords are

present/prevalent in the network

Text relations ◦  What text should be tied to other

text?

◦  2-mode: What text is tied because of common use by actors?

Text ties ◦  How is text tied to other text?

Social networks ◦  What configurations of text define the

network? ◦  2-mode: What configurations of text

tied by actors defines the network

*** This is the work in progress considering

what the text side means ***

Outcomes Actor-Text networks Collaboration

�  What information sharing is or should (according to theory, pedagogical intent) be observed?

Innovation

�  What external information is or should brought to the network?

Autonomy

�  What independent thought is or should be evidenced in the network?

SN concepts

�  Weak vs strong ties

�  Roles and positions

�  Social capital

Text Argumentation

�  What co-location/configuration of text is or should (according to theory, pedagogical intent) be observed?

Transformation

�  What change in language/concept use is or should be evident?

Emotion

�  What emotion is or should be evident?

Learning and literacy concepts

�  Collaborative learning, Transformative learning

�  Common language, Discourse communities

�  Engagement (emotion)

�  Enculturation, learning ‘to be’ an expert, a member of a group, a social media user, etc.

Three Studies (briefly – as time permits)

� Relational discovery ◦ Qualitative analysis to determine what

constituted a ‘learning tie’

� Node discovery ◦  Enhancing identification of network actors

through text analysis

� Tie discovery ◦  Identifying network connections through

common use of text

#1 Relational Discovery

0

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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: Received

Science, social science, and education teams Data = Number of pairs maintaining each type of relation

Haythornthwaite, C. (2006). Learning and knowledge exchanges in interdisciplinary collaborations. Journal of the American Society for Information Science and Technology, 57(8), 1079-1092.

Name 5-8 others with whom you work most closely on the project. “What did you learn from {each of these others}?”

Qualitative analysis of answers.

#2 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]

Issues: Actor identification Name resolution

Add Tie Weights: Distinguish important text

Example Keep in mind that google and other search technology are still evolving and getting better. I certainly don't believe that they will be as effective as a library in 2-5 years, but if they improve significantly, it will continue to be difficult for the public to perceive the difference.  

From To O r i g i n a l weight With IE

A B 1 0.5 A C 2 1.6 A D 2 2.1 A E 3 2.5 A F 1 0

Using Yahoo! Term Extractor, a sample message below returns three concepts: “google”, “search technology” and “library”.

The amount of information it transmits can be estimated as ; where 49 is the total number of words in the message.  

06.0493=

Example of how an overall informaton content weighting procedure influenced tie strengths in an ego network for a student A    

Due to the absence of important or descriptive concepts in the communication between A and F, the link between them can be ignored.    i.e.,  remove  the  «I  agree»  messages.

#3: Conversation, Collaboration, Interaction

�  Conversation ◦  Considered essential for learning ◦  Exploring text records for evidence

of interactivity, social network dynamics, and conversation levels

�  Sample ◦  8 iterations of the same course ◦  2 per semester Fall 2001 to 2004 ◦  Using message header information

•  Aim •  Use simplest most widely accessible form of data •  Determined tie based on position in conversational sequence with a

posting with the same subject line •  {nb. many caveats re the subject line use}

•  Discover interactivity patterns through text association

Haythornthwaite, C. & Gruzd, A. (Jan. 2012). Exploring patterns and configurations in networked learning texts. Proceedings of the 45th Hawaii International Conference on System Sciences. Los Alamitos, CA: IEEE.

Strength of Post : Response Pairings

•  Most pairs are connected by only one immediately following posting (57-73%)

• 17-24% on two subsequent postings; 6-11% on 3; 2-5% on 4; 0-5% on more than 4 iterations

NB. excludes consideration of multi-way interaction e.g. A<-B, C<-B, A<-C

0

100

200

300

400

500

2001A 2001B 2002A 2002B 2003A 2003B 2004A 2004B

1

2

3

4

>4

Network Structures Dichotomized at 1, 2, 3 and 4 Ties [density, undirected]

.56 .20

.07 .02

Conversational ‘turns’ Revealing a ‘core discussion’ group

Network Comparisons

Post : Response tie configurations across 4 different classes

2001A (.35) 2002A

(.32)

2004A (.38)

2003A (.14)

Revealing different class structure configurations

Words, Learning and Networks �  Who is in the network – actors ◦  Text analysis for name identification,

separation of named entities from named actors

�  What is exchanged – relations ◦  Text analysis for identification of

relations, key discussions, pivotal text or topics that connect conversations and thus the network

�  Who is talking to whom – ties ◦  Discover how conversations happen

across the the network. �  Who maintains what relations

with whom ◦  What combinations of topics/texts/

keywords, etc. create what kinds of ties between people: work, social, support; instrumental, emotional

�  From text to network structures ◦  Assess what leads to, confers, or

sustains network positions such as network stars and brokers, weak and strong ties

◦  Identify structural holes, topic lacunae and avoidance

◦  Compare networks for similarities across structure, and conversational text

�  From networks to text ◦  Use network ties to inform the

analysis of text, e.g., where close ties use a variety of terms that appear to represent the same object or topic.

Wordle from subject lines from one class

MOOC data overview and challenges

Rafa Absar

MOOC data � Courses ◦  CCK11: Connectivism and Connective

Knowledge ◦  Change11: Change 2011 ◦  PLENK10: Personal Learning Environments

Networks and Knowledge

� Not restricted to any one platform ◦  “Through out this ‘course’ participants will use a

variety of technologies, for example, blogs, Second Life, RSS Readers, UStream, etc.”

Structure of the MOOC data

Daily Newsletters

Blog posts Comments

Discussion threads Comments

Twitter posts Retweets

Overview of data

CCK11 Change11 PLENK10

Blogs 812 2486 719

Discussion Threads

68 87

Comments 306 134

Tweets 1722 5665 2121

Twitter network

Issues: Identity resolution

� Coreference resolution ◦ How to identify single identities across

platforms?

� Alias resolution ◦ How to identify two or more people with the

same alias?

Social relations and learning  

29

“… it  made  me  think  of  [an example] that  Karen  posted.. ”  

Learn

“ Anne  and  I  have  been  corresponding  via  e-­‐mail  and  she  reminded  me  that  we  should  be  having  discussion  here.."

“ [Instructor’s name], if you see this posting would you please clarify for us..”

Collaborative Work

Help

Social constructivist learning theories!Zone of Proximal Development (ZPD)!

From: Woo, Y., & Reeves, T. C. (2007). Meaningful interaction in web-based learning: A social constructivist interpretation. The Internet and Higher Education, 10(1), 15–25"

(More Knowledgeable Other)

Who are the More Knowledgeable Others in a learning community?!External indicators!•  Previous roles of leadership or expertise in a knowledge

community"•  History of publications and presentations"•  Bibliometric measures (citations)"

Internal indicators!•  Contributions to the discussion; evidence of knowledge and

expertise"

•  Mentions, references by others, quotes, retweets, etc."•  Productive roles (brokers, question-askers, critical thinkers)"

Why do we want to know who are the MKO?!Practical: !•  Organize optimal ZPD for learning sub-groups"

Analysis:!•  How do MKO contributions disseminate/resonate/

diffuse through the network?"

•  Is there a correlation between ‘expertise’ and network centrality measures?!

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