Suthers, D. D., & Chu, K.-H. (2012, April 29-May 2, 2012). Multi-mediated community structure in a socio-technical network. Paper presented at the Learning Analytics and Knowledge 2012 conference

1. Paper presentation at Learning
Analytics and Knowledge 2012
Modified (and background
removed) for SlideShare
Multi-mediated
Community Structure
in a Socio-Technical
Network
Dan Suthers & Kar-Hai Chu
University of Hawaii
Supported by the National Science Foundation

2. Learning in Socio-Technical Networks
 Learning in university settings, professional
communities, and virtual organizations is increasingly
technologically embedded
– “online,” “distributed,” “networked,” “blended”
 Fundamental question in all of these settings:
how learning and other enhancements of knowledge,
skill and capital take place through the interplay
between individual and collective agency
 Demands analyses that connect learning activity in
specific times and places with the larger socio-
technical network contexts in which they take place

3. Levels of Learning in STNs
How do social settings foster learning?
Agency Epistemologies
Who or what is the agent What is the process of
that learns? learning?
 Individual  Acquisition
 Small groups  Intersubjective meaning-
 Networks (communities, making
cultures, societies)  Participatory
 Learners participate in all “levels” simultaneously
 Need to identify the social settings (“communities” or
networks) within which learners participate
 Suthers (ijCSCL 2006)

4. Tapped In
SRI’s Network of education professionals: PD and peer
support (Mark Schlager, Patti Schank, Judi Fusco)
Since 1997: longest running educational online
community
 8 years of data (7.4G)
 20K educators/year
 800 user spaces QuickTimeª and a
decompressor
are needed to see this picture.
 50 tenants
 40-60 volunteer-run
community-wide
activities per month
 Chats, threaded discussions, wikis, resource sharing ...

6. Empirical Community Identification
Schlager: “I don’t know what communities are there”
– Organizational “tenants” and individuals who come for their
own enrichment
– Multiple forms of participation and mediational means by
which participants associate with each other
 An empirical matter:
– Don’t assume that the network constitutes one community
– Don’t assume that external communities are replicated within
the sociotechnical system
 Our Approach:
– Identify clusters (cohesive subgroups) of participants
– Interpret clusters using affiliations and other information
– Note media (chats, discussions, files) through which they
interact

7. Relevance of Mediation
 Multi-mediated: TI and other environments offer
multiple means of participation, each with their own
interactional and social affordances
 Choice of technologies by which people keep in
touch both reflects and reaffirms the relationship
between interlocutors (Licoppe and Smoreda, 2005)
 Apply this idea to collective rather than dyadic level:
communities are embedded within and make use
of technological media for interaction in ways
that reflect and reaffirm their nature
 Our approach identifies the mediational means
simultaneously with identification of cohesive
subgroups (candidate communities of actants)

8. Traces Analytic Hierarchy
 Basic needs
Activity is distributed across media:
– Reunite traces of interaction into a unified analytic artifact
Logs may record activity in the wrong ontology:
– Abstract event data to other appropriate levels of description
(interaction, mediated associations, ties)
Sequential interaction analysis and aggregate network analysis
are complementary:
– Enable mapping between these descriptions both ways
 The Traces analytic hierarchy addresses these issues
 Abstract transcript representation that collects relevant events
into a single analytic artifact
 Analytic hierarchy that supports multiple levels of analysis
 Suthers (HICSS 2011)
 Suthers & Rosen (LAK 2011)

9. Interaction Affiliations
Uptake Ties
Contingencies
Mediated Associations

10. Portion of an Associogram
discussions
actors
files

11. Tapped In Data
 Selected 2 year period of high activity
 Parsed and filtered logs of user activity involving files,
asynchronous threaded discussion forums, and quasi-
synchronous chat rooms
 Events: accessing (reading and downloading) or
contributing (posting and uploading) to one of these
three artifact types
 Filtered out:
– Private chats
– Activity in the K-12 (student) campus
– Guest accounts
– Indirect file access (portrait displays)

12. Tapped In Associogram
 40,490 vertices = actants:
– 19,842 actors (49.00%)
– 12,037 discussions (29.73%),
– 5,862 files (14.48%)
– 2,749 chat rooms (6.79%)
 229,072 edges = associations
 20,431,944 events (sum of weighted degree)

13. Weighted Degrees = Events
Artifact In-degree = “reads”, Out-degree = “writes”;
Reverse for actors
Weighted Weighted Totals
In-Degree Out-Degree
Chat 12,220,792 2,512,887 14,733,679
Rooms
Discussions 5,592,946 45,085 5,638,031
Files 54,372 5,862 60,234
Artifact 17,868,110 2,563,834 20,431,944
Totals
Actors 2,563,834 17,868,110 20,431,944

14. Tapped In Associogram
 40,490 vertices = actants:
– 19,842 actors (49.00%)
– 12,037 discussions (29.73%),
– 5,862 files (14.48%)
– 2,749 chat rooms (6.79%)
 229,072 edges = events
 20,431,944 events (sum of weighted degree)
 Average path length: 4.398
– This is bipartite graph: actor-actor path length about half!
– Largely due to Tapped In Reception (R1), normalized
betweeness centrality 0.665; weighted degree 2,511,057;
unweighted degree 18,810
– When R1 removed, average path length = 6.02

15. Finding Communities in Associogram
 TI is a network; communities are embedded
 Community: cohesive subgroup with identifiable
common identity, purpose, and/or task
 “Community detection” (modularity partitioning)
algorithm due to Blondel et al.
– Maximize intra-partition connectivity in relation to inter-
partition connectivity (NP Hard)
 Computed and visualized in Gephi (gephi.org):
– beta OSS for network analysis and visualization
– handles large graphs
 Examined properties (e.g., organizational affiliation) of
high degree nodes in each partition to interpret as
communities

16. Visualization of Partitions in OpenOrd
171 Partitions
Modularity: 0.817
 Blondel, V. D., Guillaume, J.-
L., Lambiotte, R., & Lefebvre, E.
(2008). Fast unfolding of
communities in large networks.
Journal of Statistical Mechanics:
Theory and Experiment,
http://dx.doi.org/10.1088/1742-5
468/2008/10/P10008.
 Martin, S., Brown, W. M., Klavans, R., & Boyack, K. (2011). OpenOrd: An Open-Source Toolbox for
Large Graph Layout. Paper presented at the SPIE Conference on Visualization and Data Analysis
(VDA).

17. Largest Six Partitions
P1 8452 actants P2 5826 actants
(20.87%): 6953 (14.39%): 2485
actors, 673 chat actors, 782 chat
rooms, 495 rooms, 1828
discussions, and discussions, and
331 files 731 files.
29698 edges 20459 edges
(12.96%) (8.93%)
P3, P4 2565 actants P5, P6 1251 actants
(6.33%): 851 (3.09%): 112
actors, 103 chat actors, 35 chat
rooms, 1286 rooms, 1006
discussions, 325 discussions, 98
files. files.
1630 actants 1037 actants
(4.03%): 857 (2.56%): 729
actors, 26 chat actors, 153 chat
rooms, 605 rooms, 71
discussions, 142 discussions, 220
files files

18. Interpreting P3
 2565 actants (6.33%) P3 (red-brown)
– 851 actors
– 103 chat rooms
– 1286 discussions
– 325 files
 Examine high degree actants …

20. Interpreting P3
 2565 actants (6.33%) P3 (red-brown)
– 851 actors
– 103 chat rooms
– 1286 discussions
– 325 files
 Examine high degree actants …
 All media used, with intensive
reading of discussions:
– Chat: 272,865 in, 226,561 out SRI Colleague:
– Discussion: 355,656 in, 5,976 out CoP mentoring
– File sharing: 4717 in, 325 out of new teachers
in a Midwestern
school district

21. Interpreting Largest Clusters
P1 P2
 Co-location suggests a strong relationship
 Issue of the role of the Tapped In Reception

22. High Degree Nodes
Actants of unweighted degree greater than 282. Vertex size scaled by weighted
degree. Radial layout with a non-overlapping filter

23. Interpreting P1
 Top actants (unweighted)
– R1, Tapped In Reception
– R4, public room for Tapped In’s After
School Online (ASO) events
– R10, the Floor Lobby by which one enters
rooms on the Tapped In Groups floor
 Top actants (weighted)
– R1, the Tapped In Reception: 12.29% of
all chat events in the network
– R3, the personal office of Actor F, an
educational researcher
Not a community with
– R4, the ASO Public Room.
its own purpose, but
 Many highest ranked rooms are owned rather a network for
by Tapped In and function to welcome
Legitimate Peripheral
and route newcomers or as venues for
public events open to all Participation by which
 18% of actors associated only with R1 other networks are
approached
 Overwhelmingly chat based (1M to 1K)

24. Interpreting P2
 Top Two Actants (weighted)
– Actor A: volunteer with normal
account and no affiliation, is the
most active account in the system.
– Actor B a volunteer with facilitator
status. The real-world actor was
given a second account B’ Taken
together, the real word actor B/B’
is as active as Actor A.
 Most highly ranked actors are
help desk volunteers Chat-Based After
School Online Events
 Top ranked discussions and
(tightly associated with
chats are group rooms, all of P1, separated to meet
which are used for ASO events requirements of non-
 A and B regularly facilitate overlapping modularity
these events partitioning)

25. Summary: Community Interpretations
P1 P2 After School
LPP via TI
Reception and Online
other public Events
rooms
Mixed Media Chat-based
P3, P4 P5, P6
CoP in a Language
Midwestern Arts in the US
school district; Midwest;
Discussion- Pre-service
based program in
professional Western US
development
in the Southern
US

26. Myriad of Small Clusters

27. Summary & Comments
 Purely structural (graph theoretic) computations
identified cohesive subgroups that have interpretations
as communities
 Demonstrates vibrancy of Tapped In as “transcendent
community” ( Joseph et al., CSCL 2007)
 Value to learning analytics: identify social units that
are the setting or agent of learning
 Need to try with algorithm for overlapping cohesive
clusters
– Not clique percolation ( Palla et al., Nature 2005)
– Edge communities promising ( Ahn et al., Nature 2010)
 Can “dive in” to examine activity of high-degree
actors, structure of chat sessions in rooms, etc.

28. Related Work: Chat Analysis
Find evidence for relatedness (“uptake”) between chat contributions
Structural analysis of the resulting graph
“Folding” into social network

29. Discussion
Dan Suthers
suthers@hawaii.edu
lilt.ics.hawaii.edu