social network analysis of 45000 schools: a case study about technology enhanced learning in europe

Lehrstuhl Informatik 5(Informationssysteme)

Prof. Dr. M. JarkeI5-RK-0909-1

CUELC

Ruth Breuer, Ralf Klamma, Yiwei Cao, Riina VuorikariEC-TEL 2009, Nice, France, October 1, 2009

Social Network Analysis of 45000 (50000+) Schools:

A Case Study of Technology Enhanced Learning in Europe



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Social Network Analysis Contributions to EC-TEL so far

2006 - Klamma, Spaniol, Cao, Jarke: Pattern-Based Cross Media Social Network Analysis for Technology Enhanced Learning in Europe– Media Bases as research tools for TEL– SNA as research methodology for TEL

2007 – Programme Chair 2008 - Petrushyna, Klamma: No Guru, No Method, No Teacher: Self-

Oberservation and Self-Modelling of E-Learning Communities– In-depth Analysis of a Media Base for TEL– Combination of SNA and content-based measures

2009 - Breuer, Klamma, Cao, Vuorikari: Social Network Analysis of 45000 Schools: A Case Study of TEL in Europe– eTwinning database of European cooperation between schools– SNA as a tool for teachers– Visualization and Usability



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Agenda

Social Network Analysis – Tools for teachers? eTwinng Scenario and teacher-oriented SNA tool

– Data Management – Data Analysis– Data Visualization– Comparision of tool use

Conclusions & Outlook TeLLNet project



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Social Network Analysis (SNA) [Freeman 1979, Wasserman and Faust 1994]

- Fundamentals of Graph Theory: Size, order, radius, diameter, distances- Bottom-up approach: Searching more complex structures- Structural aspects: Subgraphs, k-cliques, k-plexes, cut points, bridges, clus-

tering (coefficients), connectivity, cohesion- Special phenomena: Small-world effect, Pareto distribution- Centralities: Degree, Closeness and Betweenness centrality

Network Visualization [Brandes and Wagner 2004, Krempel 2005]

- The goal is to communicate complex information intuitively- Human perception sets limits to distinguish colors and shapes

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Social Network Analysis and Visualization



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Social Networks and Tools Most popular social networks realize only few analysis aspects

Analysis and visualization tools are not sufficient or too complexNetwork Workbench Visone [Brandes and Wagner 2004]



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eTwinning ScenarioeTwinning:• Founded in 2005 andcoordinated by theEuropean Schoolnet• Projects must be doneby two or more partnersfrom different countries• Internet platform withworkspaces and (com-munication) tools



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Data Management for the Case Study



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Data Preprocessing

8

Amount of available information:– 40450 schools, 45217 teachers, 8361 projects– not all attributes are required or should be aggregated– many data records contain uncertain or wrong information

A new relational database with tables for:– schools, teachers, projects and countries– affiliation between teachers – schools– affiliation between teachers/schools – projects

Schools Projects Teachers

0,01%

0,10%

1,00%

10,00%

100,00%45,12%

10,44%

0,08%0,05%

14,10%

46,15%

3,89%

0,01%

impossible numbers of pupilsimpossible agesunexpected valuesblank values

Noise in data records



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Statistical and Analytic Functions Statistical information from the database Already interpreted questions create networks on

demand, reduced to- “Answer-“nodes and all nodes

on shortest paths in-between- Direct neighbours- Edges are automatically deleted,

if end points are removed

SNA aspects can be applied to the current network



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Speed of growth The teacher network grows much more than the school network The country network is nearly completely connected

2005 2006 2007 20080

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

8616

9129

1848

45

1746 1814

110 0

Network growing by registration of schools

new nodes total edges2005 2006 2007 2008

0

2000

4000

6000

8000

10000

12000

14000

16000

9724

10945

13469

6411

2207 2560 2771

518

Network growing

by registration of teachers

new nodes total edges



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Visual Analytics, e.g. forthe Teacher Network of 2008

Labels and dates help to identify complete substructures

75 % of nodes are not connected



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Analysis of Subnetworks 22 complete substructures Largest component with 65 nodes, 213 edges Diameter 10, radius 5 12 cut points and 4 bridges Clustering coefficient 0,81



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Usage in the Evaluation Evaluation with

– German teachers who participate in eTwinning

– “Web Science” students from RWTH Aachen University

Teachers were– Fewer– But used the tool more

Positively rated Negatively rated



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Prior Knowledge and Handling



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Impressions ofVisualization and Statistics



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Knowledge and its Application

Teachers do not get it!



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Conclusions

SNA & visualization as tools for competencedevelopment for teachers in learning networks

eTwinning case study– Complex data management issues– Visual complexity of networks– Experimenting with web-based tools– Evaluation demonstrated

– Usefulness of the tool and the approach– Need for teacher training in theory and practice– Study effect of interventions by training

in the eTwinning database



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Outlook: TeLLNet ProjectTeachers' Lifelong Learning Networks

Project within the EU Lifelong Learning Programme 3 years duration Project idea: Competence development for teachers in

learning networks with social network analysis and scenariobuilding based on eTwinning

Partners– European Schoolnet– Institute for Prospective Tecnological Studies (IPTS) –

Joint Research Centre of the European Comission– Open University of the Netherlands– RWTH Aachen University

social network analysis of 45000 schools: a case study about technology enhanced learning in europe

Technology

schools lehrstuhl informatik

social network analysiscuelc

country network

school network

network visualization

analysis aspectscuelc

forthe teacher network

jarke i5rk