Networked Learning Practices
CAROLINE HAYTHORNTHWAITE
WORKSHOP
UTS SYDNEY
AUG 20, 2014

Introduction to Workshop
2
Part 1 (90 minutes)
 Presentation (45 minutes)
 Social Network Perspective
 Some contemporary trends in
connectivity
 A social network perspective on
connectivity
 Principles
 Complexity
 SN & information exchange,
knowledge co-construction, learning
 Exercise (30 minutes)
 Exploration of SN focus on learning :
What constitutes a learning tie?
 BREAK
Part 2 (60 minutes)
 Presentation (30 minutes)
 New Media and Learning
 Network building role of Media
 Exploring the attributes of
communication channels
 Exploring the place of different modes
ina a multiplex interaction framework
 Exercise (30 minutes)
 Discussion/brainstorming on effects of
new media on learning
And/or
 Design exercise re socio-technical
balance of pedagogical intent and
media use
 WRAP-UP

Goals of the Workshop
3
 Part 1
 To familiarize you with Social Network concepts and gain an
understanding of a Relational Perspective for research
 Warning – networks are addictive!
 To show how network perspective can be applied to
questions about learning and knowledge building –
online, offline & blended, formal, informal & non-formal
 Part 2
 To introduce how new media disrupt traditional network
connectivity, open up new opportunities, and forge new
connections
 Consider how new media change learning practices

A bit about me
My Background and Interests
 How do people work, learn and
socialize together at a distance and
through computer media?
 Communication, Collaboration,
Community
 Studies : Online Learning
Networks
 Social networks / virtual communities
 Distributed learners / e-learning
 Collaborative research teams /
distributed knowledge
 Information sharing and learning /
ubiquitous learning
 New directions
 Crowds and communities
 Social media and learning
 Learning analytics
A few theoretical orientations
 Relational perspective – who
does what with whom as the unit
of analysis
 Sociotechnical perspective –
practice, observed behaviour,
technology use, etc. arises from the
interplay of people and technology
 social informatics, organizational
informatics, community informatics

Part I: Trends
SOCIAL AND TECHNOLOGICAL NETWORK
EFFECTS ON INDIVIDUALS AND SOCIETY

A Mosaic of Trends

Transformative Trends
• Technology
enabled
• Socially
maintained
• Media
facilitated
Social
Networks
E-learning
• Networked
learning
• New literacies
• Distributed
Knowledge
• Contributory
behaviour
• Collaborative
practices
• Crowds and
Communities
Participatory
Culture
Big Data
• Analytics
• Visualization

(1) Social
Networks
More than just media
A transformation in
work and social
organization
Networks,
communities,
crowds
===============
Social Network
Analysis
- an approach, method
and vocabulary for
addressing societal
structures
 Actors such as people, groups or
organizations, tied by relations that
form networks, analyzed and
displayed as graphs
Rainie & Wellman, 2012, Networked: The new social operating system.

(2) E-Learning
More than a transfer of
learning to an online
stage
Learning unbound from
institutional structures,
embracing flow across
physical, geographical,
disciplinary boundaries
Sustained over a
lifetime, enacted in
multiple, daily instances
Mobile, learning from
and in new and different
locations as needed and
on the devices at hand.
Engaged act created
through both technical
and social decisions
A transformative movement for learning
in a networked world
Haythornthwaite & Andrews, 2011, E-learning Theory and Practice

Use of Social
Networking
Sites:
• Adults:
60%
• Non-students
18-24:
88%
• Undergrads:
86%
• Graduate
Students:
82%
• Community
College:
72%
Net Generation
College Students and Technology (data US 2010) http://www.pewinternet.org/Reports/2011/College-students-and-technology/Report.aspx

Learning in a Networked World
 Educational Institutions: Formal
 Degree based, online learning environments
 Structured curriculum, resources, roles
 Textbooks, instructors, tutors
 Informal and non-formal
 Personal, interest based, community of interest from casual to
serious leisure to non-degree based learning
 Emergent configurations and roles
 E-Learning, Networked learning, Ubiquitous learning
 Learning on and through the web
 Embedded in home, work, travel contexts
 Contributing as well as retrieving
 Collaboratively determining learning trajectories
 Working like experts rather than novices, entrepreneurial

(3) Participatory
Culture
Personal but shared need
• Creative Commons
Changes in authority
structures
• Peer production,
Peer evaluation
Differing by enterprise
• Crowds, Communities
Motivations
• Public Good, Career
Outcomes
• Social Capital,
Community Resilience,
Knowledge distribution
An opportunity to draw on the power of
crowds and the support of communities
Jenkins et al, (2006). Confronting the Challenges of Participatory Culture: Media Education for the 21st Century.

(4) Big Data
Proliferation of data
and information
streams
Dynamic, Small to
Huge
Geo-located
Needing collection,
management, analysis,
presentation, validation
Ethical, intelligent use
Data, information,
analytics and
visualization literacy
When you automate, you informate (Zuboff)

Learning Analytics
14
 Learning analytics is the
measurement, collection,
analysis and reporting of
data about learners and
their contexts, for
purposes of
understanding and
optimising learning and
the environments in
which it occurs.
https://tekri.athabascau.ca/analyti
cs
 Journal of Learning Analytics (@UTS)
 Special issues:
 Journal of Educational Technology &
Society (2012)
 American Behavioral Scientist (2013)
 Australian representatives for SoLAR:
 Simon Buckingham Shum,
University of Technology, Sydney
 Shane Dawson, University of
Southern Australia
 Grace Lynch, University of New
England, Australia
 Phillip Long (University of
Queensland, Australia)

II. SOCIAL NETWORKS,
LEARNING NETWORKS

Networks and Learning
Questions Today …
 How can network perspectives
be used to examine learning
and education processes?
 What needs to be done to build
a network analytic base for
learning?
 How can what is known in
social network research be used
to jumpstart learning networks
research?
 Social network analytic views of
learning
 Connecting this to aspects of
learning and networks that
lend themselves to a research
agenda for learning
 Interwoven with examples
related to learning and
examples from studies of
learning networks

Social Network Building Blocks
 Actors tied by relations that form networks,
analyzed and displayed as graphs

Networks are revealed in our interactions
 Personal or Egocentric
view
 Bird’s eye, helicopter
or Whole Network
view
Eddie
Fran
Fred
Ginger
Ego
Child at
home
Child at
college
Spouse
Parents
Pete
Classmates
Pat
Boss
Pam
Co-workers
Online learners Science research team

Network Perspective
Personal
 Does the individual have in
their network access to
sufficient resources?
 How is the individual engaging
with their network?
Communal
 Are there sufficient ties and
resources within the network to
support communal awareness,
action, solidarity?
 Are there sufficient external
connections to support access
to new
Answer person, and Discussion Person info. ?
(Fig 3a&3b from Welser et al, 2007)
http://www.cmu.edu/joss/content/articles/volume8/Welser/

Map of science derived from clickstream data
“Knowledge Map”
based on probability
of clicking between
journals.
(Figure 5 in Bollen et
al, 2009)
Networks
formed
by our
use of
systems
Bollen J, Van de Sompel H, Hagberg A, Bettencourt L, et al. (2009) Clickstream Data Yields High-Resolution Maps of Science. PLoS
ONE 4(3): e4803. doi:10.1371/journal.pone.0004803
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004803

Networks observable from our data traces
 Social media, point of sale, GPS
**Activist discussion: Canadian Tar Sands
(Brittany White)
**London Olympics, 2012 (Anatoliy Gruzd)
#hcsmca – Health Care Social Media Canada
Gruzd & Haythornthwaite, 2013
**Networks courtesy of the Social Media Lab, Dalhousie University http://socialmedialab.ca/

Social Network Perspective
 Not just pretty pictures
 A method for social analysis: social network analysis
 A relational approach
 Emphasis on what people do together
 Who talks to whom about what?
 Who gives, receives, shares what kinds of resources?
 Who learns from whom?
 A network approach
 Attention to network structures and their outcomes
 How does the structure of a network affect resource flow among
group members?
 When do resources reach others?
 What resources can network members access?

A moment to look at network features
Networks show
Cohesion
 Density,
Centralization,
Cliques,
Structural Holes
Actor Prominence
 Prestige,
Influence
Roles and positions
 Stars, Brokers,
Gatekeepers,
Isolates
Network outcomes
Resource Flow
 control
 inclusion and
exclusion
 early and late access
to information
Roles
 information suppliers,
help givers, social
support givers
Social structures
 Social capital,
network resilience
In-class collaboration network – who works with whom

Interactions
 Rather than aggregates of behaviors
 On average, 6000 tweets are sent per second, of these types:
 Pointless babble – 40%; Conversational – 38%; Pass-along value –
9%; Self-promotion – 6%; Spam – 4%; News – 4%
(Pear Analytics. 2,000 tweets 2009 US in English)
 Examine behaviours in terms of social interaction
 Pointless babble is ‘social grooming’ (boyd, 2009)
 Information posting via Twitter comes with expectation of
reciprocity (Holton et al, 2014)
 Actors in closer relationships (work, friendship) communicate
more often, about more things (Granovetter and others), and
via more media (Haythornthwaite & Wellman, 1996)

Under the hood: Network Data
 Who to/from whom
 Actor x Actor,
1-mode networks
 Affiliation Networks
 Actor x Events,
2-mode networks
 Can derive actor x actor,
and event x event networks
 Reveals hidden common
experience, knowledge
TO
Ava Brad Cam Dale Ed Frieda Gail Henri
FROM Ava 0 1 1 1 0 1 1 0
Brad 0 0 1 0 1 0 0 0
Cam 0 1 0 1 1 0 1 0
Dale 0 0 1 0 0 1 1 0
Ed 0 0 0 1 0 1 1 0
Frieda 0 0 0 0 0 0 1 0
Gail 0 0 0 1 0 0 0 0
Henri 1 1 1 1 1 1 1 0
TO
Corn Soy Tomatoes Carrots Peas Turnips
FROM Ava 1 1 1 1 0 1
Brad 0 1 1 0 1 0
Cam 0 1 0 1 1 0
Dale 1 0 1 0 0 1
Ed 0 1 0 1 0 1
Frieda 1 0 1 0 1 0
Gail 1 0 0 1 0 0
Henri 1 1 1 1 1 1

Let’s do a quick affiliation network
26
 Who has read these books:
 Any of Wizard of Oz, Alice in Wonderland, Peter Pan
 Watership Down
 Harry Potter (any of them)
 Goodnight Moon
 A contemporary Children’s Australian <is ‘Diary of a Wombat’ a
reasonable choice?>
 A classic Australian novel <help me name one!>
 Who has attended these conferences:
 Ascalite, Internet Researchers, CSCW, LAK … others?
 Who knows at least one person in this room?
 Who knows everyone in this room?
 … latent tie structure

Social Networks Research
27
WHAT DO WE KNOW SO FAR
ABOUT SOCIAL NETWORKS?

Some Key SNA Findings
28
 Individual/Dyadic/Triadic
 Relational multiplexity
 Strength of weak ties
(Granovetter)
 Strength of strong ties
(Krackhardt, Granovetter)
 Forbidden triangle
 Organizational
 Structural holes (Burt)
 Diffusion of innovations (Rogers)
 Gatekeepers, Technological Gurus
(Allen); Absorptive Capacity
(Cohen & Levinthal)
 Internet/Media effects
 Media Multiplexity
(Haythornthwaite & Wellman)
 Latent Ties (Haythornthwaite)
 Crowds and Communities
(Haythornthwaite )
Society
 Community lost, saved, liberated
(Wellman)
 Core discussion networks
(McPherson & Smith-Loven)
 Homophily
 Birds of a feather flock together
 Transitivity
 Tendency for our friend’s friends
to be our friends
 Inclusion/Exclusion
 Organizational work hours and
places support homophily
(Smith-Loven)
 Social mobility (Lin)
 Social capital (accessed and
mobile) (Lin)

Weak and Strong Ties
Weak Ties . . .
 Acquaintances, casual
contacts
 Tend to be unlike each other
 Travel in different social
circles
 Resource exchanges
 Infrequent, instrumental
 Few types of resources,
exchanges, relations
 Low motivation to share
 Strength of weak ties
 Experience / Information
/Attitudes comes from a
different social sphere
 But, no obligation to share
. . . Strong Ties
 Friends, close friends, team-mates
 Tend to be like each other
 Travel in the same social circles
 Resource exchanges
 Frequent, multiple types:
emotional and instrumental
 High level of intimacy, self-disclosure
 Reciprocity in exchanges
 Strength of strong ties
 Motivated -- obliged -- to share
what resources they have
 But, access to same resources

Societal Connections
Community Lost, Saved & Liberated,
and now Networked (Wellman)
 Lost. Lament for the passing of the
pastoral ideal of community, lost in
urbanization
 Saved. Rediscovered local community
amid the towers of urban living
 Liberated. Social network based
(Wellman, 1979) – place independent,
liberated from geography, sustained
through phone and travel
 Networked – the New Operating
Systems (Rainie & Wellman, 2012)
 Personal communities – networked
individualism – sustained through
ICT, networked living, wireless
connectivity
 Neo-liberated. Finding career, work,
friends, homophilous others through
computer networks
 Hyper-liberated. Unbound by
boundaries of organizations and
traditional workplaces
 Free of constraints of single career,
employer, institution
 learning within institutional
boundaries (e.g., MOOCs)
 human capital resource location
 single author/ publisher/curator
 Community now found in myriad
multi-threaded instances

Networks Structures
31
ACTORS / NODES
RELAT IONS / EDGES
T IES
NETWORKS

Actors
 Individuals
 Adults, teens, children
 Employers, employees,
co-workers
 Collectives
 Groups* or Teams
 Organizations
 Communities*
 Other
 Countries, Governments,
Schools, Websites,
Documents
 Individuals
 Teachers, students
 Schools, universities
 Co-workers, collaborators,
team mates
 Collectives
 Research teams
 Professional organizations,
clubs
 Communities,
neighborhoods, societies
 Online groups
 More?
NOTE: A group in SNA is defined as a
highly interconnected clique. Thus Groups
– and I also maintain Communities – are a
hypothesis to be tested.

Actor Roles and Positions
 Centrality. Network Star
 Betweenness. Bridge, Broker
 Prominence
 Influence, Prestige
 Equivalence
 Identical ties to and from others
or to and from equivalent others
 E.g., teachers of same class, or
teachers of equivalent classes in
different schools
 Roles
 Technological guru
 Troll
 Information provider
 Learner-leader, facilitator
 Answer or discussion person
 Who dominates
conversation?
Who seeds it?
 Who suggests new
resources?
 Who controls the
flow of
information?
 Who does everyone
ask? And about what?
 Who does everyone listen to? And
about what?
 Who gives emotional support?
 Who disrupts, diverts, obstructs
discussion?
 What matters for teaching and
learning, or in learning
communities?

Relations: Content, Direction & Strength
 Content. Physical,
emotional, or informational
 Chat - gossip, ‘social
grooming’
 Advice
 Instruction
 Collaboration - work, learning,
play
 Social support –major or
minor emotional support
 Trust
 Services
 Small to large: babysitting,
lending money, cleaning up
after disasters, helping
neighbors
 Direction of resource flow
between actors
 Giving or Receiving
 Strength of the relation
 How much, how often, and
how important
 Intimacy, Frequency,
Intensity, Quantity,
Regularity, Longevity, Value
 Defined both objectively and
subjectively
 Minor versus major social
support
 Daily, weekly, monthly
communication

Learning Relations
 Learning
 Know-what: facts from teachers, books, etc.
 Know-how: apprenticeships, informal learning
 Fiction: contagious diffusion of gossip and rumour
 Group: practices, who knows what (transactive memory), who knows who
knows what
 Education
 Teaching, learning
 Evaluation: giving/handing in assignments, giving/ receiving grades
 Delivery of information: giving/attending lectures,assigning/reading
materials
 Community
 Social support for learning, technology use
 Teaching by experts, learning by novices
 Learning community practices: culture, society, behavior, etc.

Analyzing the Relational Mix
 Asking relational questions to address learning
relationships and structures
 Who talks to whom, about what? And via which media?
 What relations are maintained by actors who report a learning tie?
 How does a learning tie differ from a work, social or collaborative tie?
TYPE OF INTERACTION Group Members: 1 2 3 ... 20
How often have you received instructions (i.e., exact
directions on what work to do) from this person?
in unscheduled face-to-face meetings
in scheduled face-to-face meetings
by telephone
by fax
by electronic mail
by videoconferencing
How often: D for daily W for Weekly M for Monthly Y for Yearly 0 for never
For in between amounts use e.g., 2D for twice a day, 6Y for six times a year

Analyzing the Relational Mix (3 examples)
 1. Co-located Computer Science Department
 25 respondents (of 35 member group) answered 24 questions about a
variety of their work and social interactions with 10-20 others within
the group
 Asked about relations and type of work and friendship tie
 Factor analysis revealed six dimensions of work and
social interaction reflecting
 Work practices : Receiving work (engaged in by 57% of pairs); Giving
work (57%)
 Major work products : Collaborative Writing (32%); Computer
Programming (56%)
 Social support relations : Sociability (86%); Major Emotional
Support (7%)

Analyzing the Relational Mix
 2. Interdisciplinary Research
Teams
 3 teams: science, social science,
education; qualitative and semi-structured
interviews;transcripts
coded for learning exchanges
 Who do you learn from or
receive help in understanding
something from? (and Who learns
from you)
 Nine categories of learning
 Major: Factual (Field) knowledge;
Process (how to) knowledge;
Method; Joint research
 Minor: Technology knowledge ,
Socialization; Generation of new
ideas, Networking, Administration
[very minor]
Data = Number of pairs
maintaining each type of relation

Analyzing the Relational Mix
 3. Science Teachers (54)
 What did you learn from the 5-8 others with whom you
communicate most frequently about your area of science and
science teaching
 Five codes derived from content analysis of
questionnaire responses
 Science teaching techniques
 Science content
 Class and behavior management
 Matters external to their school
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
 School and administrative function

Relations define Ties
 From Weak to Strong show increases in:
 Number and types of interaction
 Intimacy and reciprocity
 Attention and commitment to the relationship
 Frequency of interaction
 Number of means of communication used
 Motivation to share information and resources

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
 Questions
• How do you bring peripheral actors
into the learning community?
• What is the right mix of tie strength
to sustain innovation and
commitment?

Networks: Structure
 Cohesion
 Density: # actual ties to
possible ties
 Centralization: extent
organized around a central
core:
 Cliques, clusters,
components
 Reach
 Can every network member
be reached by some path
 Path length to get
information around the
network
In-class
communication
networks:
•Chat
•Discussion board
•Email

Networks of Networks
 Knowledge transfer from
“community-embedded
learning” (Kazmer, 2007)
 Local community 
classmates and online learning
community
 Course knowledge
  learner’s workplace
  learner’s home community
 One community  another
 through contact in the e-learning
community
 One institution of higher
learning  another
 through contact in the e-learning
community Teacher networks
across schools (top:
EnLiST project;
bottom: De Laat, 2010)

Learning Ties
45
EXERCI SE
- - - DEF INING LEARNING TIES - - -
- - - READING NETWORK S TRUCTURES - - -

Learning Scenarios
46
You started a learning initiative with the aim of creating connections among group
members so the community will become self-sustaining. You want to see if the
effort has worked. What will you look for in connection between actors to show
connectivity outcomes?
• Each group choose one of the scenarios from the options below
• define a (realistic) outcome you want**
• determine a definition of a tie that matters to this outcome
• determine what (one or more relations) you will ask about (OR analyze
transcripts for) as evidence of this interactional learning outcome
Small online
class (15-25)
Workplace with
strong norms
and procedures
Big online class of
1000s (e.g., a
MOOC)
Open learning
community
Examples of outcomes:
Common knowledge, New
knowledge, Innovative thinking,
Group cohesion, Shared resources,
Cooperation, Collaboration,
Collaborative learning,
Shared practice

What Constitutes a Learning Tie?
Which interaction, for what
outcome?
 Fact/ know-what. Received
from teachers, texts
 Fiction. Contagious diffusion of
gossip and rumour
 Know-how. Apprenticeships,
observation, non-formal
learning
 Group processes. Norms and
practices
 Informal learning
 Group knowledge. Who knows
what; Who knows who knows what
Let’s add to this list and ideas
 What level of attention?
 Individual, dyadic, small group,
institution, community, society
 Education Relations
 Teaching, learning
 Reviewing, evaluating
 Collaborative learning
 Community/Societal Relations
 Social support for learning,
technology use
 Learning community processes and
practices
 Societal distributions of resources,
access and knowledge
 Did we consider re tie behavior
the role of:
 Trust
 History of actors, of the network
 Future expectations of association

Reading Networks
48
Clockwise:
online class;
2 x
workplaces;
xMOOC;
open
learning
community

Further Reading
49
 Haythornthwaite, C. & Andrews, R. (2011). E-learning Theory
and Practice. London: SAGE.
 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: Enhanced Learning and
Teaching (pp. 352-374). IGI Global.
 Andrews, R. & Haythornthwaite, C. (2007). Introduction to e-learning
research. In R. Andrews & C. Haythornthwaite (Eds.),
Handbook of E-Learning Research (pp. 1-52). London: Sage.
 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/
 See also: http://haythorn.wordpress.com/