adding the best_paper_award

README.md

 
 # Table of Contents
 
-1.  [CSEDU-2019](#orgbf8e38b)
-    1.  [Images](#org8ac626a)
-    2.  [Code](#org96cf4dc)
-        1.  [Hangout chats:](#orgcba8365)
-        2.  [Moodle visualization:](#orgec74280)
-        3.  [Coursera](#orgcd541bc)
+1.  [CSEDU-2019](#org5968523)
+    1.  [Images](#org789fb42)
+    2.  [Code](#orgc54c525)
+        1.  [Hangout chats:](#org4f380ad)
+        2.  [Moodle visualization:](#orgaac2801)
+        3.  [Coursera](#orgc546d66)
 
 
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 # CSEDU-2019
 
-Resources and annexes to the paper **"[Towards Visual Explorations of Forums' Collective Dynamics in Learning Management Systems](tex_draft.pdf)"**  (This paper received the best student paper award)
+Resources and annexes to the paper **"[Towards Visual Explorations of Forums' Collective Dynamics in Learning Management Systems](tex_draft.pdf)"**  (This paper received the [best student paper award](./images/best_paper_award2.jpg))
 
 Checkout the [presentation](mkone_CSEDU2019_diapo.pdf) 
 
 
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 ## Images
 
@@ -76,7 +76,7 @@ The images in the paper are sometime small.  We offer high enough resolution ima
        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
 
@@ -85,25 +85,25 @@ It is mainly code to preprocess the data files that will then be processed in d3
 The exemples of visualisations are accessible online at the following adresses (<span class="timestamp-wrapper"><span class="timestamp">&lt;2019-05-22 mer.&gt;</span></span>):
 
 
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 ### Hangout chats:
 
 Visualizations on <https://observablehq.com/@maliky/conversation-visualization-v2>
 
 
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 ### Moodle visualization:
 
 Visualizations on <https://observablehq.com/@maliky/playing-with-moodle-data>
 
 
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 ### Coursera
 
-Use the file [Code/SNA-Coursera17/](Code/SNA-Coursera17/) folder to scrape the data from a Coursera course and then wrangle the data to make the visualisation.
+Use the 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<sub>tool.py</sub>
-Check the [Code/SNA-Coursera17/README.md](Code/SNA-Coursera17/README.md) 
+Check the <Code/SNA-Coursera17/README.md>