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Enhancing Distance Learning Cohesion through Social Network Analysis

This study explores the application of Social Network Analysis (SNA) to measure and enhance cohesion in collaborative distance learning environments. By analyzing interaction data from the "Simuligne" online learning course, we demonstrate how SNA can identify cohesive groups, isolated individuals, and communication patterns among participants. Our findings highlight the importance of social relationships for motivation and collaboration in online education, providing insights into effective pedagogical strategies and tools for improving learner engagement.

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Enhancing Distance Learning Cohesion through Social Network Analysis

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  1. How Social Network Analysis can help tomeasure cohesionin collaborative distance-learning Christophe REFFAYThierry CHANIER Laboratoire d’Informatique de Franche-ComtéBesançon - France

  2. Outline • Introduction • Experiment : Simuligne learning session • SNA computational models • Cliques • Clusters • Conclusion

  3. The problem • Face-to-face: • Visual & Oral indices Distance: Interaction data

  4. Hypothesis CL works well in « Active » groups. Collaboration requires communication Questions synthesis from communication data appropriateness importance computability Representation Our approach:

  5. How SNA can help ? Social Network Analysis, based on: • Group dynamics • Social relationships models • Graph theory

  6. The central role of "Cohesion" • Necessary for collaborative tasks • Very important for social aspects • Essential for motivation (no isolation)

  7. Cohesion ? much more complex than… temperature, speed, or weight… …Less physical, and more human ! Cohesion is an attractive "force" between individuals

  8. Cohesive subgroups SNA (Wasserman & Faust, 1994) : "Subsets of actors among whom there are relatively strong, direct, intense, frequent or positive ties"

  9. Experiment : "Simuligne": distance learning course

  10. Research Ministry of France Programme COGNITIQUE 2000 (53 K€) ICOGAD Project Analysis of interaction tracks from SimuLigne Definition of needed indicators to follow a group Development of new tools to plug in LMS partner partner leader Research context : the ICOGAD project Simuligne : The training session Simuligne : The training session

  11. Pedagogical hypothesis To produce together Learning

  12. The learning context • 100% at a distance • French as foreign language • Public : 40 adults • English speakers • Advanced level in French • Web litterate • Groups of 10 + tutor + 2 NS • LMS : WebCT • 30 hours over 10 weeks

  13. Tutor Tutor Tutor Tutor Learners Learners Learners Learners The Simuligne organisation Coordinator Aquitania Gallia Lugdunensis Narbonensis

  14. Simuligne Interaction data • e-mail : 834753 chars in 4062 messages • Forum : 879015 chars in 2686 messages • Chat : 234694 char. in 5680 speach turns

  15. Communication graphs • Only read (opened) messages. • (separately) on E-mail or Forum • for a given period • Gives the number of messages sent by A and read by B on the directed edge A->B

  16. Gallia E-mails over the whole training period

  17. The E-mail Graph matrix For Gallia over the whole training period

  18. E-mails Gallia (without the tutor)

  19. A forum graph (Gallia) … Not very useful information

  20. Forum Matrix Not straightforward to use...

  21. Our first try… Global index of cohesion (Group) • 0 for no relation • Based on shared neighbours • 1 for a fully connected graph • Number of messages ignored • Difficult to define evolution • No information on individuals But:

  22. 8 8 Clique of level c (valued graph) Def. : A clique of level c is a set where all members are directly connected one to another with a value  c. C = 10 Clique of level 10 13 12 12 15 10 10 11 11

  23. 5 12 Gl2 7 10 Gl4 32 14 10 Gt Gn2 Gl1 8 Gl6 7 Gn1 Gl10 Computing cliques of level-c • Symetrisation of the adjaccency matrix • Definition of the threshold (c) • Selection of ties >= c=10

  24. Gl2 Gl4 Gt Gn2 Gl1 Gl6 Gn1 Gl10 Computing cliques of level-c Each pair of members of the resulting subset exchanged at least 10 messages. Property: This is a cohesive subset

  25. Result on Gallia for c=10

  26. Comparing the 4 groups Aquitania Gallia Gallia Narbonensis Lugdunensis

  27. Information given by Cliques • A good picture of the group structure • Highlights cohesive groups • Highlights isolated individuals … for a given threashold c !

  28. Hierarchical Clusters Initially : identity partition : N clusters Repeat Find the most communicant pair of clusters Fusion of the pair in one cluster Print communication level (k) N = N-1 Until (N=1)

  29. Max Min Hierarchical Clusters for Gallia GALLIA G G G G G G G G G G l l l n l G l n l l l 1 Level 3 2 1 1 t 4 2 6 5 9 0 ----- - - - - - - - - - - - 167 . . . XXX . . . . . . 108 . . . XXXXX . . . . . 83 . . XXXXXXX . . . . . 64 . . XXXXXXXXX . . . . 52 . XXXXXXXXXXX . . . . 42 XXXXXXXXXXXXX . . . . 29 XXXXXXXXXXXXXXX . . . 9 XXXXXXXXXXXXXXX XXXXX 5 XXXXXXXXXXXXXXXXXXXXX

  30. Discussion • Cliques of level c gives: • precise communication structure (for a given c). • cohesive subsets • isolated individuals. • Clusters: • show more about intensity • Easier to compare groups.

  31. Technical Conclusion • Cliques and clusters: complementary information • Process • Clusters analysis on all groups • Then threshold c • Level-c cliques

  32. Further work • User friendly representations • Development • Experiment • SNA multiplexity: integration of all com tools into one representation • Exploration of SNA models

  33. Questions ?

  34. From data to social indices • LMS data extraction • relationshipdefinitions (graphs) • Apropriate model • User friendly representation

  35. E-mails of Aquitania during the whole training period

  36. Aquitania (without the tutor)

  37. (questions) Activity as a whole • A LMS is an integration of many tools • The designer can use them differently : • Communication (Forum, e-mail, Chat, …) • Production (texts, drawing, cards, etc…) • Tests (quizzes, auto-evaluation,…) • Reading, contents pages navigation, etc. • One learner can participate to many courses • To reckon cohesion only on forum is not sufficient in general, but a good starting point in “simuligne”

  38. Gl3 Gl2 Gl4 Gl5 Gt Gn2 Gl1 Gl9 Gl6 Gn1 Gl10 Using the Cliques of level c C=10

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