Present

1. TeLLNet
Learning Analytics in a Teachers’
Social Network
Manh Cuong Pham, Yiwei Cao,
Zinayida Petrushyna, and Ralf Klamma
RWTH Aachen University
Advanced Community Information Systems (ACIS)
last_name@dbis.rwth-aachen.de
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
I5-PCPK-0412-1 This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

2. TeLLNet Advanced Community Information
Systems (ACIS)
Responsive
Web Engineering Community
Web Analytics
Open
Visualization
Community
and
Information
Simulation
Systems
Community Community
Support Analytics
Lehrstuhl Informatik 5
Requirements
(Information Systems)
Prof. Dr. M. Jarke
I5-PCPK-0412-2
Engineering

3. TeLLNet
Agenda
 Introduction to social capital
 Social network analysis for social capital
 Case study: social capital in eTwinning network
 Conclusions and future work
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
I5-PCPK-0412-3

4. TeLLNet
Introduction
 Human capital vs. social capital [Burt, 1992]
– Human capital: the personal ability to perform tasks (e.g. talent, education, etc.)
– Social capital: the social environment surrounding individuals
 Social capital as a property of
– Individuals: positions in social network that are more efficient in performing tasks
(i.e. local structure)
– Groups: structure of members’ network that makes the group functions more
efficient (i.e. structure of a sub-network)
 In our research, we study social capital in teachers’ network
– By SNA metrics and a development model
– The performance of teachers and projects: recognized by Quality Labels
– Network structure of projects and position of teachers: identified via networks
created by several communication mechanisms (e.g. message, project
Lehrstuhl Informatik 5
(Information Systems)
collaboration, blog)
Prof. Dr. M. Jarke
I5-PCPK-0412-4

5. TeLLNet Social Capital:
Structural Hole vs. Closure
 Structural holes [Burt, 1992]
- Nodes are positioned at the interface between
groups (gatekeepers, e.g. node B)
- Informational advantages: access to
information from different parts of networks
- Form novel ideas by combining information
from different groups
- Control the communication between groups
 Closure
- Nodes are embedded in tightly-knit groups (e.g. node A)
- More trust and security within coherent communities
 Social capital [Coleman, 1990]
- Individuals and groups deriving benefits from social relationships
Lehrstuhl Informatik 5
(Information Systems) - Network structural property: either structural hole or closure
Prof. Dr. M. Jarke
I5-PCPK-0412-5

6. TeLLNet Identification of Individual
Social Capital
 Given the network G=(V,E), where V is the set of nodes and E is the
set of edges
 Structural holes: nodes with high betweenness
u
(i, j )
B(u )
u i j (i, j )
where: u
(i, j ): number of shortest paths between nodes i and j that pass through
node u
(i, j ): total number of shortest paths between nodes i and j
 Closures: nodes with high local clustering coefficient
v, w N(u) : (v, w) E
C(u)
N(u) N (u ) 1 / 2
Lehrstuhl Informatik 5 where: N (u ) is the set of neighbors of node u
(Information Systems)
Prof. Dr. M. Jarke
I5-PCPK-0412-6

7. TeLLNet Identification of Group
Social Capital
A community development model [Pham et al., 2011]
Lehrstuhl Informatik 5
 In which stage is the members’ network of a given group?
(Information Systems)
Prof. Dr. M. Jarke
I5-PCPK-0412-7
 How does it relate to the performance of the group?

8. TeLLNet Qualify the Stage of Group
Member Network
 Density: fraction of actual edges in the network
v, w V : (v, w) E , n is the number of nodes
D n
2
 Global clustering coefficient
3 number of triangles
D
number of connected triples
 Maximum betweenness: highest betweenness of nodes
 Largest connected component: fraction of nodes in largest connected
component
 For large member networks
Lehrstuhl Informatik 5
- Diameter: the longest shortest path between any pair of nodes
(Information Systems)
Prof. Dr. M. Jarke - Average shortest path length
I5-PCPK-0412-8

9. TeLLNet Case Study:
eTwinning Community
Data #data entries Description
Project 23641 Schools from at least two schools from at least two different European countries create a
project and use ICT to carry out their work.
Contact 769578 Teachers are able to explore other teachers' profiles and add them into their own contact
list. It is suggested to use forum and other media to contact the other teachers before
taking them as a contact.
Project diary 20963 Blog for project reports
Project diary post 49604 Each blog entry in project diary
Project diary 7184 Comments added to blog entries in project diary
comment
My journal 38496 Message posted on teachers' wall which is part of teachers' profile
message
Teacher 146105 Registered teachers working in European schools and, namely "eTwinner"
Quality label 8042 Awarded first to projects. Then the project-involved schools and teachers are awarded
accordingly. They are assigned by each country or on the European level: National Quality
Label and European Quality Label
Prize 1384 eTwinning Prizes are awarded to schools. They are of European level and are called
European eTwinning Prizes
Institution 91077 Various European schools: pre-school, primary, secondary and upper schools
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
Statistics on eTwinning data (as of 11.11.2011)
I5-PCPK-0412-9

10. TeLLNet
eTwinning Network
Network #nodes #edges Description
Project 37907 804856 Nodes are teachers (eTwinners) and there is a connection (edge) between two
(26%) (0.11%) teachers if they collaborated in at least one project. Edges in the network are
undirected and weighted by the number of projects in which the two teachers
collaborate.
Contact 109321 573602 Nodes are teachers and there is an edge between two teachers if at least one
(75%) (0.01%) teacher is in the contact list of the other. Edges are undirected and unweighted.
Project diary 3264 3436 Nodes are teachers and there is an edge between two teachers if one teacher has
(2.2%) (0.06%) commented on at least one blog post created by the other. Edges are directed and
weighted by the number of comments.
My journal 23919 30048 Nodes are teachers and there is an edge between two teachers if one teacher has
(16%) (0.01%) posted or commented on the wall of the other. Edges are directed and weighted by
the number of messages.
Teacher networks statistics (as of 11.11.2011)
 Data is processed, transformed and loaded into Oracle data warehouse
 Networks are aged for time series analysis
 Network parameters are computed using Oracle store procedures
Lehrstuhl Informatik 5
(Information Systems)  Projects are considered as groups to study group social capital
Prof. Dr. M. Jarke
I5-PCPK-0412-10

11. TeLLNet Properties of Teacher Networks:
The Power Law Degree Distribution
Project network degree distribution Contact network degree distribution
4 6
Raw data Raw data
1.455 1.933
2 y=28.209x 4 y=327.630x
2
Cumulative Frequency
Cumulative Frequency
0
0
2
2
4
4
6
6
8 8
10 10
0 2 4 6 8 0 2 4 6 8 10
Degree Degree
Project diary network degree distribution My journal network degree distribution
6 6
Raw data Raw data
1.625 1.750
4 y=14.904x 4 y=38.875x
Cumulative Frequency
Cumulative Frequency
2 2
0 0
2 2
4 4
6 6
8 8
0 1 2 3 4 5 6 0 1 2 3 4 5 6 7
Degree Degree
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
Degree distribution of eTwinning networks follow the power law with the formula y ax
I5-PCPK-0412-11

12. TeLLNet
Social Capital of Teachers
(a) Quality labels and number of projects/posts/contacts/wall posts (b) Quality labels and degree
1 1
Project diary Project diary
0.9 0.9 Contact
Contact
0.8 Project 0.8 Project
My journal My journal
0.7 0.7
0.6 0.6
Frequency
Degree
0.5 0.5
0.4 0.4
0.3 0.3
0.2 0.2
0.1 0.1
0 0
0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70
Number of quality labels Number of quality labels
(c) Quality labels and betweenness (d) Quality labels and clustering
1 1
Project diary Project diary
0.9 Contact 0.9 Contact
Project Project
0.8 0.8 My journal
My journal
0.7 0.7
Betweenness
0.6 0.6
Clustering
0.5 0.5
0.4 0.4
0.3 0.3
0.2 0.2
0.1 0.1
0 0
0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70
Lehrstuhl Informatik 5 Number of quality labels Number of quality labels
(Information Systems)
Prof. Dr. M. Jarke
I5-PCPK-0412-12
Structural hole as a form of social capital in eTwinning networks

13. TeLLNet Projects Achievement and
Non-structural Properties
Quality label of projects and their properties Quality label of projects and their properties
1 0.9
Country Teacher
0.9 Language 0.8 Institution
Fraction of received quality label projects
Fraction of received quality label projects
Subject
0.8
0.7
0.7
0.6
0.6
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1 0.1
0 0
0 5 10 15 20 25 30 35 40 0 10 20 30 40 50 60 70 80
Number of countries involved, languages used and subjects Number of teachers and institution involved
 Number of countries and languages used somehow correlate to the quality
 Number of teachers and institutions: effect on small projects (less than 30 members)
Lehrstuhl Informatik 5
(Information Systems)  Subject has no effect
Prof. Dr. M. Jarke
I5-PCPK-0412-13

14. TeLLNet Projects Achievement and
Structural Properties
Quality label of projects and their members network parameters
0.5
Density
Clustering coefficient
Maximum betweenness
0.45
Largest component
Fraction of received quality label projects
0.4
0.35
0.3
0.25
0.2
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Network parameters
 Project member networks: created using the previous project collaboration and wall
messaging, reflect the early communication of project members
 High quality projects prefer the Bonding stage: consists of seperated densely connected
groups
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke  Form of social capital: structural hole
I5-PCPK-0412-14

15. TeLLNet
Conclusions and Future Work
 Social capital in eTwinning Network
– Both teachers and projects follow structural hole
– The informational diversity is the key success factor
 Applications: recommendation tools
– Help teachers find projects, contacts, etc.
– Help project organizers find, select and invite project partners
 Future works
– Tracking the development pattern of teacher networks
– Tracking the development pattern of teachers for competence management
– Developing tools
– Recommendation tools
– Dynamic visualization of local and global teacher networks as well as network parameters
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
I5-PCPK-0412-15