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"ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
"Daniel D. Suthers" <[log in to unmask]>
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"Daniel D. Suthers" <[log in to unmask]>
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CFP reminder: Priority Consideration due Monday Feb 21; Late 
proposals also considered ...


A workshop held in conjunction with the 9th International Conference 
on Computer Supported Collaborative Learning

July 4-8, 2011, The University of Hong Kong, Hong Kong, China
(Workshop to be held Tuesday July 5th)

* Daniel Suthers, University of Hawaii
* Christopher Teplovs, University of Toronto
* Maarten de Laat, Open Universiteit Nederland
* Jun Oshima, Shizuoka University
* Sam Zeini, University Duisburg-Essen


This workshop is for researchers in CSCL and related fields who seek 
to understand how learning takes place in the interplay between 
individual, small group and collective (community or networked) 
levels of activity in "online" or Information and Communication 
Technology-mediated settings. We will address concomitant questions 
concerning how theories at multiple levels of analysis articulate 
with each other or explain phenomena across levels, and how methods 
for local analysis (e.g., process oriented microanalysis) can be 
coordinated with methods for global analysis (e.g., structural social 
network analysis). Examples of relevant research questions include:

* How does learning takes place through the interplay between
  individual and collective agency?
* How are local phenomena (individual and small group activity)
  aggregated (e.g., via stigmergy) to lead to emergent phenomena
  that create resources, contexts, or value that are then
  available for individual and small group learning?
* How are advances in community knowledge (knowledge building)
  driven by local activity?

The workshop will examine theoretical and methodological approaches 
to these questions in conjunction with each other, for example:

* Do the different levels of analysis need different theories
  that then must be articulated with each other?
* Are there theoretical perspectives that themselves bridge
  the levels of analysis?
* How can (for example) sequential analysis of interaction,
  content analysis and social network analysis be coordinated
  to address these issues?
* How can we use aggregate levels of analysis to figure out
  where to "dive in" for local analysis, for example to make
  sense of results at the aggregate community level, or to
  find local sources of innovation?
* What practical techniques such as different forms of
  triangulation or visualization techniques help connect
  the different levels of analysis?


This will be a one day workshop. It will include framing 
presentations by organizers and, according to the submissions we 
receive, analyses of shared data corpora by several analysts, and 
small group and full group discussion of related topics. Potential 
participants may request one of the following roles:

Data Presenter/Analysts: Researchers who have a corpus that
  potentially illustrates learning phenomena at multiple
  levels and can make it available to other participants
  in advance. They will summarize the data (setting, how
  collected, etc.) and present their own multi-level analysis.

Analyst: Researchers who have approaches to analyzing learning
  at multiple levels who will be given access to the data and
  conduct their own analyses for presentation in the workshop.

Discussant: Researchers who have theoretical and/or
  methodological orientations that might inform the question
  of connecting levels of analysis, and are asked to comment
  on what we learn from the above analyses.

Concept Presenters: Participants who are not prepared or not
  selected to play the above roles but who have some concept
  at theoretical or methodological levels that organizers judge
  to be of potential value. They may be asked to give brief

Basic Participants: Persons with an interest in the topic but
  who are not prepared or not selected to play the above roles.
  They will be welcome to participate in all open discussion.


All interested researchers should submit up to two pages summarizing 
their relevant prior experience, their objectives in participating in 
this workshop, and a bibliography of relevant publications or an URL 
providing further information on the researcher's work. The abstract 
should indicate which of the types of participation discussed above 
is requested: Data presenter/analyst, Analyst, Discussant, brief 
Concept presentation, or Basic participation. Those who propose 
Concept Presentations should submit one additional page on a concept 
that can be presented in 5-10 minutes. Researchers who wish to be 
selected as Analyst or Discussant should submit up to two pages 
characterizing their theoretical and/or methodological approach to 
connecting levels of learning. Potential Data Presenters should meet 
the requirement for Analyst and also submit up to two pages 
summarizing the nature of the corpus and making the case that this 
data will serve the objectives of the workshop. Data Presenters are 
encouraged to contact organizers in advance to discuss the corpus.

Send applications (PDF preferred, but other common document formats 
are acceptable) to Dan Suthers suthers [at] and Chris 
Teplovs chris.teplovs [at] Submit by February 21, 2011 for 
priority consideration. Late applications will be considered on a 
space-available basis. Acceptance decisions will be sent out by March 
15, 2011. Data Presenters will be expected to provide data to 
Analysts by April 15, 2011.

Daniel D. Suthers

Dept. of Information and Computer Sciences
University of Hawai'i at Manoa
1680 East West Road, POST 309
Honolulu, HI 96822 USA
(808) 956-3890 voice  <-- When requiring action within few days
(808) 741-5686 mobile <-- For issues requiring immediate action
(808) 956-3548 fax
mailto:[log in to unmask] <-- May require a week or two to read

Chair, Communication and Information Sciences PhD Program
Professor, Department of Information and Computer Sciences
Laboratory for Interactive Learning Technologies
Assoc. Editor, Int. J. Computer Supported Collaborative Learning

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