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* CSEDU-2019
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Resources and annexes to the paper *"[[file:tex_draft.pdf][Towards Visual Explorations of Forums' Collective Dynamics in Learning Management Systems]]"*
** Images
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 The images in the paper are sometime small.  We offer high enough resolution images to see the details.
**** Figure 1
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[[file:images/discussion.png]]

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 Illustration of how the strength of actors' ties (or links) varies as a function of time and topic overlap.  Thread 1 corresponds to actor-topic dynamic (\ref{eq:1a}) where B's late post after A's 1st publication does not correlate strongly enough to create the link from A to B.  But A's 2nd post is timely enough, although not exactly on the same topic as B's message, to create the tie A --> B drawn as a dashed arrow.  In thread 2, in addition to the tie B --> A, we have a topic overlap and time proximity between C and A.  This makes a the strong tie A \to C.
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**** Figure 2
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[[file:images/fu2.png]]
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   iForum's Dashboard \citep{Fu2017} showing (a) overall changes of post in the forum, (b) a thread representation, (c) discussions in packed forms, (d) the social network and (e) the details of a discussion.

**** Figure 3
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[[file:images/convis2.png]]
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   Convis Dashboard \citep{Hoque2016} helps explore conversations.  On the left, the topics found in the forum using \gls{lda} and organized hierarchically.  In the middle, the colored rectangles show a sentiment analysis for each message.  Each message is linked to his author place on a semi-circle and thus creating a social network.  Finally, on the right, Convis display the detail of the conversation.

**** Figure 4
[[file:images/func.png]]
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 Graphical proposition for the function I(s,r) of the messages' interaction strength.  s is the ``internal'' strength of two messages based on content, time and social network structure.  r is ``external'' requirement, it is a parameter set by the observer.
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**** Figure 5
[[file:images/cycles.png]]

   Interactions cycles built from a bi-party actor-topic graph Thread 3 and 4 are transformed to an actor-actor graph.  Dotted arrows denotes weaker links.

**** Figure 6
[[file:images/pipeline.png]]
Data Analysis cycle.

**** Figure 7
[[file:images/dynco_portrait.png]]
   Visualizations from the FFL dataset.

**** Figure 8
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[[file:images/uvci_portrait.png]]  
There is also [[https://idev.kone.ci/visu/uvci/conv7][this link]] to the onlined and live version.

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   Detail of \gls{vuci} conversation taking place in 2 hours.  Each circle is a message, and hovering over them brings up its content.  The circle's size is proportional to their content's length.  Message are layered vertically by actors.  On the left is an indication of the actors total messages count.

**** Figure 9
[[file:images/evolution.png]]
   At the top (a) is half of compound yearly actor-actor network.  The three bottom images (b), (c) and (d) are closeup around actor 642 during the quarters of the year. 
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** Code
The images above have been generated using code that ressembles closely the one below.
It is mainly code to preprocess the data files that will then be processed in d3.js.
The exemples of visualisations are accessible online at this adresses (<2019-05-22 mer.>):

*** Hangout chats:
Visualizations on https://observablehq.com/@maliky/conversation-visualization-v2
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*** Moodle visualization: 
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Visualizations on https://observablehq.com/@maliky/playing-with-moodle-data
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*** Coursera
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Use the file [[file:Code/SNA-Coursera17/]] folder to scrape the data from a Coursera course and then wrangle the data to make the visualisation.
The visualisation requires graph_tool.py
Check the [[file:Code/SNA-Coursera17/README.org]]