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

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)

3. #1
Educational background

4. educational environments
• contact-based, offline
• network (internet) supported
• blended
• distance education
• virtual education

5. learning environments
• personal (PLE)
• social interactivity-based (web2)
• personal activity-based (MOOC)
• instructional (LMS, LCMS)

6. current trends
• growth of information sharing space
• permanent possibility of the interaction
• the efficiency depends of the environment

7. #2
Social Network Analysis

8. SNA theoretical background

10. online information sharing network

11. we calculate, we use,
it's important for us:
• directed matrix
• whole network
• realized connection
• edge weight
• timeline of connections
• information type
• information quality

12. what's not important:
• personal (offline) friendship
• (other) social network relationship
• offline information sharing
• sharing to the group

13. #3
Analyzed groups

14. 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

15. #4
Descriptive graphs

16. Inf2013 group
(MSc, offline classroom, "MOOC" N=21, E=39)

17. Tav2012 group
(MSc, hybrid environment, N=42, E=147)

18. konnekt2012 group
108 days, 6743 action in the group (mean = 62,44)

19. konnekt2012 group
(MSc, connectivism, N=43, E=429, T=6743)

20. konnekt2012 group
quality interactions
N=32, E= 215
social media noise
N=40, E= 318

21. #5
curve estimation,
function analysis

22. number of edge (realized information connections)

23. #6
learners, groups -
differences

24. frequent statistic measures:
• degree centrality
• shortest path
• betweenness centrality
• closeness centrality
• diameter (longest shortest path)
• eigenvector centrality!
• local clustering coefficient
• graph density

25. (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."

26. 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

27. 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

28. 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

29. 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