Information Sharing and Interaction in the Online Learning Communities

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June 5, 2014
ELTE PPK Takács Etel Room (KAZY 407)

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Information Sharing and Interaction in the Online Learning Communities

  1. 1. Information Sharing and Interaction in the Online Learning Communities János Ollé Eötvös Loránd University Faculty of Pedagogy and Psychology Department of Education Information Society Teaching and Researching Group June 5, 2014 ELTE PPK Takács Etel Room (KAZY 407)
  2. 2. #1 Educational background
  3. 3. educational environments • contact-based, offline • network (internet) supported • blended • distance education • virtual education
  4. 4. learning environments • personal (PLE) • social interactivity-based (web2) • personal activity-based (MOOC) • instructional (LMS, LCMS)
  5. 5. current trends • growth of information sharing space • permanent possibility of the interaction • the efficiency depends of the environment
  6. 6. #2 Social Network Analysis
  7. 7. SNA theoretical background
  8. 8. online information sharing network
  9. 9. we calculate, we use, it's important for us: • directed matrix • whole network • realized connection • edge weight • timeline of connections • information type • information quality
  10. 10. what's not important: • personal (offline) friendship • (other) social network relationship • offline information sharing • sharing to the group
  11. 11. #3 Analyzed groups
  12. 12. nickname content sem level type method N social network analyse konnekt2012 information society 2012 spring MSc offline lecture +seminar connectivism 59 facebook OK tav2012 distance education 2012 spring MSc offline lecture, virtual 3D hybrid environment 65 facebook, 3D SecondLife OK bevikt2013 ICT, web2, online social 2013 autumn BSc offline seminar offline interactivity 14 facebook OK tav2013 distance education 2013 spring MSc offline lecture offline classroom, “MOOC” 53 facebook few interaction infotud2013 information society 2013 spring MSc offline lecture offline classroom, “MOOC” 21 facebook OK bevikt2012 ICT, web2, online social 2012 autumn BSc offline seminar offline interactivity 44 facebook few interaction ossz2013 all of above +research methodology 2013 autumn BSc MSc PhD lecture, seminar “one big group” 117 google plus few interaction sportinf2012 ICT, web2, online social 2012 autumn BSc offline seminar offline interactivity 52 facebook few interaction
  13. 13. #4 Descriptive graphs
  14. 14. Inf2013 group (MSc, offline classroom, "MOOC" N=21, E=39)
  15. 15. Tav2012 group (MSc, hybrid environment, N=42, E=147)
  16. 16. konnekt2012 group 108 days, 6743 action in the group (mean = 62,44)
  17. 17. konnekt2012 group (MSc, connectivism, N=43, E=429, T=6743)
  18. 18. konnekt2012 group quality interactions N=32, E= 215 social media noise N=40, E= 318
  19. 19. #5 curve estimation, function analysis
  20. 20. number of edge (realized information connections)
  21. 21. #6 learners, groups - differences
  22. 22. frequent statistic measures: • degree centrality • shortest path • betweenness centrality • closeness centrality • diameter (longest shortest path) • eigenvector centrality! • local clustering coefficient • graph density
  23. 23. (Abraham-Hassanien-Snasel, ed. 2010) "Eigenvector centrality is a measure of the importance of a node in a network." "A player’s degree of “popularity” within the network, i.e., they represent centers of large cliques in the graph. A node with more connections to higher scoring nodes is considered as being more important."
  24. 24. content sharing res- ponse inter- action relevant content mixed content irrelevant content other content eigen-vector r! p 0,165 0,079 0,070 0,462 0,036! 0,707 -0,137! 0,146 0,098 0,298 0,043 0,648 0,260 0,005 • the "importance in the network" does not correlate with relevant content sharing activity • It correlates with other content sharing activity (for example: social noise, pretence activity) • is social media really useful in educational communication?! • hopefully: there are differences between different methodology used groups eigenvector - content sharing activity
  25. 25. sum inter- actions posts com- ment content sharing res- ponse inter- action relevant content mixed content irrelevant content other content eigen- vector r! p 0,039 0,677 0,167 0,075 0,064 0,502 0,165 0,079 0,070 0,462 0,036! 0,707 -0,137! 0,146 0,098 0,298 0,043 0,648 0,260! 0,005 konnekt2012 0,034! 0,815 0,010! 0,946 0,023! 0,875 0,004! 0,976 0,028! 0,974 0,005! 0,974 -0,190! 0,186 -0,043! 0,767 0,070! 0,631 0,126! 0,382 tav2012 -0,040! 0,801 -0,106! 0,502 -0,066! 0,676 -0,114! 0,472 -0,067! 0,672 -0,036! 0,819 -0,070! 0,662 -0,120! 0,451 -0,026! 0,872 -0,109! 0,493 infotud2013 0,664! 0,001 0,592! 0,005 0,669! 0,001 0,593! 0,005 0,652! 0,001 0,564! 0,008 0,392! 0,079 0,577! 0,006 0,501! 0,021 0,592! 0,005 • the "importance in the network" does not correlate with relevant content sharing activity - any groups • the online activity are useful only in the open, regular course group ("little MOOC") • there are the social media noise significance as well eigenvector - content sharing activity
  26. 26. conclusions of correlation matrix • small groups, small networks - small conclusions :) • #1 online communities (social networks) can be very spectacular but are not useful for educational process • they can help the communication, but there are a big irrelevant social noise • there are differences between groups • #2 connectivism in small groups can develop "social market", but connected knowledge is unsure • #3 hybrid environment (offline, online, 3D) obviate the bridge role in the information network • #4 in an open online course the activity can be better quality than other groups
  27. 27. Thank you, for the attention and for your patience! János Ollé Eötvös Loránd University Faculty of Pedagogy and Psychology Department of Education Information Society Teaching and Researching Group

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