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)

#1 Educational background

educational environments• contact-based, offline

• network (internet) supported

• blended

• distance education

• virtual education

learning environments

• personal (PLE)

• social interactivity-based (web2)

• personal activity-based (MOOC)

• instructional (LMS, LCMS)

current trends

• growth of information sharing space

• permanent possibility of the interaction

• the efficiency depends of the environment

#2 Social Network Analysis

SNA theoretical background

online information sharing network

we calculate, we use, it's important for us:

• directed matrix

• whole network

• realized connection

• edge weight

• timeline of connections

• information type

• information quality

what's not important:

• personal (offline) friendship

• (other) social network relationship

• offline information sharing

• sharing to the group

#3 Analyzed groups

nickname content sem level type method N social network analyse

konnekt2012 information society

2012 spring MSc

offline lecture

+seminarconnectivism 59 facebook OK

tav2012 distance education

2012 spring MSc

offline lecture,

virtual 3Dhybrid 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

lectureoffline classroom,

“MOOC” 53 facebook few interaction

infotud2013 information society

2013 spring MSc offline

lectureoffline classroom,

“MOOC” 21 facebook OK

bevikt2012 ICT, web2, online social

2012 autumn BSc offline

seminar offline interactivity 44 facebook few interaction

ossz2013all 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

#4 Descriptive graphs

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

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

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

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

konnekt2012 group

quality interactions N=32, E= 215

social media noise N=40, E= 318

#5 curve estimation, function analysis

number of edge (realized information connections)

#6 learners, groups -

differences

frequent statistic measures:• degree centrality

• shortest path

• betweenness centrality

• closeness centrality

• diameter (longest shortest path)

• eigenvector centrality!

• local clustering coefficient

• graph density

(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."

content sharing

res-ponse

inter-action

relevant content

mixed content

irrelevant content

other content

eigen-vector r!p

0,1650,079

0,0700,462

0,036!0,707

-0,137!0,146

0,0980,298

0,0430,648

0,2600,005

• 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

sum inter-

actionsposts com-

mentcontent sharing

res-ponse

inter-action

relevant content

mixed content

irrelevant content

other content

eigen-vector

r!p

0,0390,677

0,1670,075

0,0640,502

0,1650,079

0,0700,462

0,036!0,707

-0,137!0,146

0,0980,298

0,0430,648

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

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

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