LAK13 Tutorial Social Network Analysis 4 Learning Analytics

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Slides of the tutorial "Computational Methods and Tools for Social Network Analysis Networked Learning Communities" at the LAK 2013 in Leuven.

Tutorial Homepage:
http://snatutoriallak2013.ku.de/index.php/SNA_tutorial_at_LAK_2013

Conference Homepage:
http://lakconference2013.wordpress.com/

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LAK13 Tutorial Social Network Analysis 4 Learning Analytics

  1. 1. Computational  Methods  and  Tools  for  Social  Network  Analysis  of  Networked  Learning  Communities Tutorial at LAK 2013, 9/4/2013Andreas Harrer, Tilman Göhnert,Alejandra Martínez-Monés &Christophe Reffay
  2. 2. Agenda 13.30 Introduction of presenters and participants13.45 Use Cases of SNA4LA14.15 SNA Basics15.15 Description of the practical workbench15.30 COFFEE BREAK16.00 Hands-on experiences using the workbench17.30 End of the tutorial9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities2
  3. 3. Introduction  of  presenters  &  participants 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities3
  4. 4. Tutorial  Presenters •  Andreas Harrer•  Tillman Göhnert•  Alejandra Martínez-Monés•  Christophe Reffay9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities4
  5. 5. Use  Cases  of  SNA  for  LA 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities5
  6. 6. Identifying  Participatory  Roles  in  CSCL  scenarios Marcos García, J.A.,  Martínez Monés, A.,  Dimitriadis, Y.,  AnguitaMartínez, R. A role-based approach for the support ofcollaborative learning activities e-Service Journal. 6(1):40-58,Diciembre 20079/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities6
  7. 7. Participatory  roles •  Goal: Identifying roles based on theirposition within a network of relationshipso  Description of expected roles, based on centrality indexeso  Identify the emergence of those roles in an experienceo  Provide them with information adapted to their needs•  Approach:o  Description of roles based on “fuzzy” combinatios of SNAindexes9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities7
  8. 8. Participatory  roles    (Distance  forum,  CSCL  2009) IsolatedNon-participative
  9. 9. Role: Dynamizer studentIndicatorsOutdegree CDo(i)Description Number of links initiated by this actor.V a l u e s /InterpretationA high value, indicates a high participation ofthe actorR e l e v a n c erankFirstOutdegree sessionsDescription Specifies the relation between participationand number of sessionsV a l u e s /InterpretationA high value indicates a high participation ofthe actor in the overall activityR e l e v a n c erankSecondIndegree CDi(i)Description Number of links terminating by this actorV a l u e s /InterpretationA medium value indicates a mediumrelevanceR e l e v a n c erankThird.Participatory  roles  Dynamizer  student 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities9
  10. 10. Student  dynamizer    (Distance  forum,  CSCL  2009) AnimatorCDo(B20) = 16CDo-sessions (B20) = 30,8%CDi (B20) = 4 (17th value)solatedNon-participative
  11. 11. Student  dynamizer    (Web-­‐‑based  document  sharing) 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities11aalobelabalarraordboaarodullcfergoncgeivegcgonzrolcjimcabemonvegemunsei epadgonesasbazestibalizggjibalalailizmarimunadojjimriojorgelcaravilconaselhergarLmunblamarnmarMcamalomferrubmlaurothmmaygommmiggutncalguaNoeliapapajimplagvelppersanproferapaduqrfueotergorvilrmarcolrpermarRumbramscilramscunfersfermarsmarmorvdieferVmaybarAnimator
  12. 12. Cohesion  in  subgroups   Reffay, C. and Chanier, T., (2003) How social network analysiscan help to measure cohesion in collaborative distancelearning, Proc of CSCL, 2003Reffay, C., Teplovs, C., & Blondel, F.-M. (2011). Productive re-use of CSCL data and analytic tools to provide a newperspective on group cohesion. Proc of CSCL 2011.9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities12
  13. 13. Cohesion •  Simugline data seto  4 online groups working on an French as foreign language simulationo  Each group had an instructor and a•  Datao  Discussion forums that are local to each of the 4 groups•  Networko  The relation between “a” and “b” represents messages sent by “a” andopened by “b” plus messages posted by “b” and opened by “a”•  Indexeso  Cliques at level “c”: subgroup in which the ties between all pairs of agentshave values c or greater (i.e., have exchanged c or more messages).o  “c” can be a value announced by the teacher as the desirable level ofinteraction9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities13
  14. 14. Comparing groups with (level10) cliquesAquitaniaGalliaLugdunensisGalliaNarbonensis9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities14
  15. 15. Hierarchical ClustersGALLIA GG G G G G G G G G ll l n l G l n l l l 1Level 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 XXXXX5 XXXXXXXXXXXXXXXXXXXXX9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities15
  16. 16. PaNern  (Star)  =>  Intensity? AquitaniaLugdunensis NarbonensisGalliaIntensity=192Intensity=12 Intensity=72Intensity=1119/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities16
  17. 17. The  “fourth”  man Malzahn, N., Harrer, A., & Zeini, S. (2007). The Fourth Man -Supporting self-organizing group formation in learningcommunities. In Proc. of CSCL 2007 (pp. 547–550).9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities17
  18. 18. 18Person-­‐‑Topic-­‐‑Network  from  Forum:  group  searches  for  the  „fourth  man“ 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities
  19. 19. 19Network  using  semantic  relations 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities
  20. 20. Blockmodeling Harrer, A. & Schmidt, A. (to appear 2013). Blockmodeling androle analysis in multi-relational networks. Social Networks andMining. Springer. 20139/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities20
  21. 21. 21Complex  networks  –  dissolving  the  Death  Star 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities
  22. 22. 22Complex  networks  –  dissolving  the  Death  Star 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities
  23. 23. 23Complex  networks  –  dissolving  the  Death  Star 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities
  24. 24. A  Blockmodel  of  this  network  –  positions  and  reduced  matrix 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities24
  25. 25. SNA  basics 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities25
  26. 26. SNA  basics •  What is a Social Network?•  Types of networks and network transformations•  Useful definitions and measures on graphs•  Grouping concepts9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities26
  27. 27. What  is  a  social  network? •  A set of nodes (actors)o  Personso  Groupso  Organizationso  Objectso  …•  A set of relationshipso  Is a friend ofo  Is neighbour ofo  Provides goods to …o  Has sent a message to …o  Etc.9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities27
  28. 28. What  is  a  social  network? •  Complexity mayincrease.•  Analysis cannotbe done byhand9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities28
  29. 29. Ego-­‐‑net  :  The  network  of  ego •  Ego: the selected node•  Alters (neighbours): distance (Ego,Alter) ≤ 1o  Ties between ego and altero  Ties between altersWhole network Ego-net (x34)Ego-net (x38)9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities29
  30. 30. Types  of  Social  Networks  According  to… •  Number of sets of actorso  One-mode : one set of actorso  Two-mode : (Bi-partite, affiliation networks) two sets of actors•  Relationshipso  Directed or undirectedo  Valued or un-valued (1/0)•  How are they builto  Complete networkso  Ego-networks9/04/13Computational Methods and Tools for Social Network Analysisof Networked Learning Communities 3030
  31. 31. One-­‐‑mode  or  two-­‐‑mode  networks All nodes are of the same type•  Administrators•  SocietiesTwo-modeOne-mode9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities31Nodes belong to twosets•  Students
  32. 32. Directed  vs  Undirected  graphs •  DirectedUndirectedEdges  are  oriented Edges  are  not  oriented 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities32
  33. 33. Weighted  (valued)  vs  Unvalued  graphs •  Weighted/Valued •  UnvaluedEdges  have  values Edges  have  no  value 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities33
  34. 34. Conclusion:  8  possible  network  types One-mode(One node type)Two-mode(Two node types)• One-Mode• Directed• Valued• One-Mode• Undirected• Valued• One-Mode• Directed• Unvalued• One-Mode• Undirected• Unvalued• Two-Mode• Directed• Valued• Two-Mode• Undirected• Valued• Two-Mode• Directed• Unvalued• Two-Mode• Undirected• Unvalued9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities34
  35. 35. Network  types  transformation  allowed Two-­‐‑Mode One-­‐‑Mode Directed Undirected Valued Unvalued More  information Less  Information Selection strategyNot reversible!9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities35
  36. 36. Two-­‐‑Mode One-­‐‑Mode 221111Do blue nodes share any orange resource? => UnvaluedHow many orange resource do blue nodes share ? => ValuedStrategy: Decide what sharing resource represent for relationships between (blue) nodes.9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities36
  37. 37. Directed Undirected Are nodes connected (one tie is enough)?Are nodes connected with reciprocal edges?Strategy: Decide if you have/not edges in both directions.9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities37
  38. 38. Valued Unvalued Threshold=5Strategy: Only ties with value>=Threshold are considered9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities38
  39. 39. Useful  measures  of  social  networks •  Density•  Degree, In-degree, Out-degree•  Path, Geodesic distance, Diameter•  Centrality indexes (for nodes)o  Degree centralityo  Betweenness centrality,o  Closeness centrality9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities39
  40. 40. Eff.=0Poss.=10d=0Eff.=2Poss.=10d=0.2Eff.=4Poss.=10d=0.4Eff.=8Poss.=10d=0.8Eff.=10Poss.=10d=1Density  (of  edges)  for  an  undirected  graph edgespossiblenbedgeseffectivenbddensity =9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities40
  41. 41. Density  (of  edges)  for  a  directed  graph Eff.=0Poss.=20d=0Eff.=4Poss.=20d=0.2Eff.=8Poss.=20d=0.4Eff.=16Poss.=20d=0.8Eff.=20Poss.=20d=1Reciprocal edges count twice(twice more possible edges)9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities41
  42. 42. 1 42 65 83 7Net A124 5683 7Net BThe  structure  as  a  constraint   Do nodes “4” and “5” have the same role in nets A and B?Density:DA=9/28=0,321Density:DB=9/28=0,3219/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities42
  43. 43. Centrality•  Who is central in thisnetwork?439/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities
  44. 44. Degree  in  an  undirected  graph •  For a node, Degree = number of edges9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities44
  45. 45. In-­‐‑  &  Out-­‐‑  degree  in  an  directed  graph In-­‐‑degree    =  number  of  edges  coming  into  the  node Out-­‐‑degree    =  number  of  edges  coming  out  of  the  node 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities45
  46. 46. Path  :  sequence  of  edges  connecting  2  nodes AHBC GDE F I JFrom A->E : 2 possible paths:• (A B C E)or• (A B D E)Example in a directed graph9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities46
  47. 47. Path:  example  in  an  undirected  graph AHBC GDE F I JFrom A->E : 2 possible paths:• (D E)or• (D B C E)Geodesic Distance:Length of the shortest pathd(D,E) = 19/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities47
  48. 48. Diameter  of  the  graph •  Diameter = longest distance in the graph= maximal distance between any pair of nodesWhat is the diameter of this graph?D = 79/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities48
  49. 49. Betweenness  centrality   •  Number of shortest paths passing through the nodeDirected graphUndirected graph9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities49
  50. 50. Closeness  centrality   Scoring the closeness of one node to all othersUndirected graphDirected graph9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities50
  51. 51. The Moreno’sexperiments (1943)Pupils relation in theclassroom:•  Pupils of various agerange•  Gender study« If you could choose freely,which are the (2) kids youwould like to have asdirect neighbour? »Main results:At <age> => pupils tend to <?>• 6-8 years old => mix• 8-13 years old => separate• 13-15 years old => mix• 15-17 years old => separate9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities51
  52. 52. Moreno’s  network •  Who is central in this network?9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities52
  53. 53. Components   Removing  bridges    (cut-­‐‑points)… 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities53
  54. 54. …This  results  in  breaking  the  component   9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities54
  55. 55. Grouping  concepts  –  an  overview Groups can be determined according to different criteria•  Reachability and Distance – group member isconnected via short ways to all other group memberso  Direct links – Clique as complete subgrapho  Relaxing the distance – n-Clique requires all nodes being connected via shortpath (lesser and equal than n)•  Node degree – group member should be connected tomany group memberso  Leaving out a small number of group members: k-Plexo  Having at least k group members as direct neighbours – k-Core•  Contrasting “ingroup” and “outgroup” – density inside ismuch higher than outsideo  Alliance: only links to ingroup, no links to outside9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities55
  56. 56. Grouping  concepts  –  an  overview •  Group concepts fall in two categories:o  Overlapping concepts•  e.g. Cliqueso  Disjunct concepts•  e.g. k-cyclic blocks•  Depending on the type of analysis both categorieshave their meritso  Disjunct concepts allow clear-cut assignment to one groupo  Overlapping concepts allow analysis of transfer ideas, e.g. Cliquepercolation9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities56
  57. 57. Cliques  or  K-­‐‑cliques   Clique: maximum subset where allnodes are connectedK-clique: Clique with K membersHow  many    cliques? • One 5-clique• One 4-clique• One 3-clique• Three 2-cliques=> 6 cliquesWhich  are…  ? 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities57
  58. 58. K-­‐‑cores 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities58Taken from: V. Batagelj, A. Mrvar / Social Networks 22 (2000) 173-186
  59. 59. Clique  Percolation  Method •  CPM allows overlapping communities•  Idea: a k-clique “percolates” through the graph•  Overlapping members can be “brokers” betweengroups9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities59Taken from: Wikipedia
  60. 60. Visualization  influences  Interpretation 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities60
  61. 61. Practical  Workbench    Presentation 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities61
  62. 62. Task  one:  Simuligne  Data  Preprocessing 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities62Raw Data: A networkwith weighted, directededges(Number of forum postsopened)Preprocessing:Symmetrisation of edgeweights(by minimum, maximum,sum, or average)
  63. 63. Task  one:  Simuligne •  Choose the data set based on preprocessingo  Narbo_Max: Maximum of both directionso  Narbo_Mean: Average of both directionso  Narbo_Min: Minimum of both directionso  Narbo_Sum: Sum of both directions•  Think of the format transformation (UCINET -> SISOB)•  Focus on the appropriate intensity level of therelation•  Identify groups•  Choose an appropriate output representation9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities63
  64. 64. Task  two:  Collaboration  over  Artifacts  (BSCW) 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities64•  Two node typeso  BSCW (document sharing) folders as artifacts (-..)•  One folder for general information•  Folders for individual case studieso  Pairs of students, each working mainly on a single case (x..)•  Edges weighted by access
  65. 65. Task  two:  Collaboration  over  Artifacts  (BSCW) 9/04/13Computational Methods and Tools for Social NetworkAnalysis of Networked Learning Communities65•  Choose one of the data setso  sp1_B_cli_cp_U.txto  sp2_B_cli_cp_U.txto  sp3_B_cli_cp_U.txto  spf_B_cli_cp_U.txt•  Think of the format transformation (UCINET -> SISOB)•  Try to identify the general folder•  Try to identify the projects the pairs of studentsworked on•  Analyse the collaboration between the students(hint: Folding is also in the R-Analysis)

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