Interaction, Mediation, and Ties: A Framework for Multi-Level Analysis of Distributed Interaction (presented at the workshop on Connecting Levels and Methods of Analysis in Networked Communities at the Learning Analytics and Knowledge Conference 2012, Vancouver)

1. Presentation at the Workshop on Connecting
Levels and Methods of Analysis in Networked
Interaction, Communities at the Learning Analytics and
Knowledge Conference 2012, Vancouver
Mediation, and (Version edited for Slideshare)
Ties
A Framework for
Multi-Level Analysis of
Distributed Interaction
Dan Suthers
University of Hawaii
Supported by the National Science Foundation

2. Preview
 Motivations
– Analytic Challenges of Technologically-embedded
interaction
– Phenomena at simultaneous granularities
(individual, small group, network) interact
 A framework for representing data at multiple
levels in a connected way
– Maps from events to contingencies, uptake,
associations, ties
 Examples of analyses at different levels
– Automating contingency/uptake analysis of chats
– “Community detection” by finding cohesive
mediated subgroups

3. Traces Analytic Hierarchy
(“Traces” is our NSF-funded project)
 Basic needs
– Reunite traces of interaction into a unified analytic artifact
– Abstract event data to other appropriate levels of description
(interaction, mediated associations, ties)
– 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
Let’s look at the concepts, then the representations ….

4. Concepts
 Contingencies: (Suthers Dwyer, Medina & Vatrapu, ijCSCL 2010)
– Manifest relationships between acts and their setting
(including other events)
– Includes media structures (e.g., “reply-to”), temporal and
spatial proximity, lexical overlap, semantic overlap …
– Evidence for Uptake (what we really care about)
 Uptake: (Suthers, ijCSCL 2006)
– Taking some aspect of (the trace of) a prior act or event as
relevant for ongoing activity
– A generalized unit of analysis for “interaction” broadly
understood (multi/cross-media; inter/intra-subjective)
 Mediation and Associations
– All interaction is mediated; actors are associated via media
– We want to understand how social phenomena are
technologically embedded ( Licoppe & Smoreda, Social
Networks 2005)

5. Origins in a detailed manual analysis of multimodal collaboration
Suthers Dwyer, Medina & Vatrapu, ijCSCL 2010
Nathan Dwyer
Richard Medina
Ravi Vatrapu

6. Traces
Analytic
Hierarchy
 Suthers, HICSS 2011;
 Suthers & Rosen, LAK
2011

7. (this portion of presentation is an
Omnigraffle animation stepping
through the Traces analytic
hierarchy as it was explained
verbally …)

36. Examples
Analyses of Tapped In
 Chat structure
 Technology embedded “communities”
 Relationships (time permitting)

37. 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 ...

39. Exposing Chat
Features Through
Analysis of
Uptake Between
Contributions
Dan Suthers
Caterina Desiato
HICSS 2012
Kar-Hai Chu
Nathan Dwyer

40. Motivations
Embedding of learning and work in socio-technical
networks leads to questions such as:
 Where are the most engaged discussions?
 Who are the central actors in these discussions, in
terms of promoting discussion by others?
 What ideas receive the most development?
 How does the interplay between individual and
collective agency lead to desirable outcomes?

41. Sequential Analysis
Sequential structure of interaction is relevant
 Engagement is displayed when actors take up each
other’s contributions.
 Central actors can be identified by how their
contributions are taken up by others.
 Identification of the development of ideas requires
tracing out threads of discussion
Human analysis is slow: can we automate
 The installation of contingencies
 Their combination into uptake
… sufficiently well to find useful structure?

42. Formative Case Analysis
First we did a manual study to compare human
analysis to rule-based (automatable) analysis,
in order to improve the latter
 After School Online Session on mentoring in
the schools with genuine engagement by
participants in addressing professional issues
 Human interpretative analysis of uptake
 Rule-based installation of contingencies
(temporal, actor, address & reply, lexical),
combined into uptake

43. Example Transcript Portion
184 23:35 Maria: are all good teachers good mentors?
185 23:38 Andrea: some people will take a while to get to that point
186 23:42 Andrea: No..not all
187 23:51 Nancy: definitely not
188 23:55 Helen: Training can help, but I think some is personality
189 24:09 Ashley: some people are excellent teachers but are horrible mentors
190 24:09 Nancy: some great teachers can not hold a decent conversation with an
adult
191 24:11 Andrea: i had to co-ops who would be awful mentors
192 24:24 Helen: Nods
193 24:27 Lisa: That is an interesting question Maria, ... I would probably say
'yes' first off, and then wonder some more
194 24:42 Maria: it is something I have thought about often Lisa
195 24:47 Andrea: I think its alot of personality
196 25:17 Lisa: one thing a mentor has to know is how to operate with a peer,
and ow to be intentional about handing over, or encouraging
greater independence
197 25:18 Maria: observation has made me think that it takes an extra “special
ingredient” to tip the scales
198 25:34 Nancy: I think if you have the passion for teaching you will want
everyone else to feel the same
199 25:35 Andrea: agree

44. Contribution Uptake and Sociogram
• Structural correlations
about 0.5 for uptake
graph, but 0.9 for
proximity prestige
• Led to rule improvement

45. Automating Contingency Analysis
Work in progress
(demonstration
available)
Example following:
24 hours in ASO
Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring and
manipulating networks. International AAAI Conference on Weblogs and Social Media.

46. (Here showed software process in
other tools …)

47. Contingency Graph, 24 Hours in ASO
• Colors are Actors
• Nodes are chat
contributions (size is
weighted in-degree)
• Links are weighted by
contingencies (evidence
for uptake)

48. Contingency Graph, 24 Hours in ASO
• Recoloring for
modularity classes
(cohesive subgroups)
• Clearly shows phases of
interaction

49. Folded Sociogram
Nodes are actors
Node size is page rank. Colors are modularity classes

50. One Month of Activity, ASO Room

51. Multi-Mediated
Community
Structure in a
Socio-Technical
Network
Dan Suthers
Kar-Hai Chu
LAK 2012
(first talk on
Monday!)

52. Finding Communities in Associogram
 TI is a network; communities are embedded
 Associogram of Actors and Artifacts (Chats,
Discussions, Files): ~40K nodes, 229K edges
 Gephi.org:
– beta OSS for network analysis and visualization
– handles large graphs
 “Community detection” (modularity partitioning)
algorithm due to Blondel et al.
 Examine properties (e.g., organizational affiliation) of
high degree nodes in each partition to interpret as
communities

53. Visualization: Fruchterman-Reingold
Choosing the right algorithm …
A classic force-directed
QuickTimeª and a
layout algorithm … run
decompressor
are needed to see this picture.
for 48 hours on a quad
core machine …

54. Better Visualization: OpenOrd
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).

55. Top 6 Cohesive Subgroups
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/10.1088/1742-5468/2008/10/P10008.

56. High Degree Nodes

57. Community Interpretations
Associations After School
via TI Online
Reception and Events
other public
rooms
CoP in a Chat-based
Midwestern Language
school district; Arts in the US
Discussion- Midwest;
based Pre-service
professional program in
development Western US
in the Southern
US Let’s look at this in Gephi …

58. (a brief demonstration of interpreting modularity classes)

59. Myriad of Small Groups

60. Exploring how
Technology
Mediates
Relationships in
Socio-Technical
Systems
Kar-Hai Chu
 Dissertation

61. Overview
 Motivation: Selection and timing of media
reflects and reaffirms status of interpersonal
relationships (Licoppe & Smoreda, 2005)
 RQ: How does technology mediate
relationships that are formed in sociotechnical
systems?
– Describe mediated interactions in terms of how
they are embedded in the technology

62. Method
 Interactions mediated by 3
artifact types
– Files
– Discussions
– Chats
 Find associogram and create vector for each
pair
 Perform cluster analysis on the vectors to find
‘types’ of relationships
– Stepwise, iterating for hierarchical breakdown

63. Interpretations of Clusters
 2.2 = good friends,
balanced relationship (high
volume) 1 97.6% 2.4% 2
 2.1 = long-term
peers/colleagues (high
volume)
 1.2 = short-term
peers/colleagues 89.2% 10.8% 95.4% 4.6%
– Leader/followers exist
here 1.1 1.2 2.1 2.2
 1.1.3 = acquaintances
– Leader/followers exist
here
 1.1.2/1.1.1 = very low 77.1% 13.9% 9.0%
frequency of interaction
1.1. 1.1. 1.1.
(no relationship) 1 2 3

64. (slides from dissertation in
progress removed pending
publication)

65. Tying together the levels
Example scenario
 Compute contingencies --> sociogram on all
scheduled chat sessions
 Identify sessions with desired structural characteristics
(e.g., high participation, role balanced)
 Microanalysis of selected sessions
 Identify persons playing roles (via both microanalysis
and sociograms) in learning-relevant events
 Are these global roles?
 How did they come into the roles?
 What communities do they participate in?
 Via what media do the relevant interactions take
place?

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