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TeLLNet
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. JarkeI5-PCPK-0412-1 This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
Learning Analytics in a Teachers’ Social Network
Manh Cuong Pham, Yiwei Cao, Zinayida Petrushyna, and Ralf Klamma
RWTH Aachen UniversityAdvanced Community Information Systems (ACIS)
TeLLNet
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. JarkeI5-PCPK-0412-2
Responsive Open
Community Information
Systems
Community Visualization
and Simulation
Community Analytics
Community Support
Web A
nalytics
Web
Eng
inee
ring
Advanced Community Information Systems (ACIS)
Requirements Engineering
TeLLNet
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. JarkeI5-PCPK-0412-3
Agenda
Introduction to social capital Social network analysis for social capital Case study: social capital in eTwinning network Conclusions and future work
TeLLNet
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. JarkeI5-PCPK-0412-4
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 collaboration, blog)
TeLLNet
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. JarkeI5-PCPK-0412-5
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- Network structural property: either structural hole or closure
TeLLNet
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. JarkeI5-PCPK-0412-6
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
where: : number of shortest paths between nodes i and j that pass through node u
: total number of shortest paths between nodes i and j Closures: nodes with high local clustering coefficient
where: is the set of neighbors of node u
jiu
u
ji
jiuB
),(
),()(
),( jiu
),( ji
2/1)(N(u)
E w)(v, :N(u) wv,C(u)
uN
)(uN
TeLLNet
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. JarkeI5-PCPK-0412-7
Identification of Group Social Capital
In which stage is the members’ network of a given group? How does it relate to the performance of the group?
A community development model [Pham et al., 2011]
TeLLNet
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. JarkeI5-PCPK-0412-8
Qualify the Stage of Group Member Network
Density: fraction of actual edges in the network
, n is the number of nodes
Global clustering coefficient
Maximum betweenness: highest betweenness of nodes Largest connected component: fraction of nodes in largest connected
component For large member networks
- Diameter: the longest shortest path between any pair of nodes- Average shortest path length
triplesconnected ofnumber
trianglesofnumber 3D
n2
E w)(v, :V wv,D
TeLLNet
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. JarkeI5-PCPK-0412-9
Case Study:eTwinning Community
Data #data entries DescriptionProject 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 diaryProject diary comment
7184 Comments added to blog entries in project diary
My journal message
38496 Message posted on teachers' wall which is part of teachers' profile
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
Statistics on eTwinning data (as of 11.11.2011)
TeLLNet
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. JarkeI5-PCPK-0412-10
eTwinning NetworkNetwork #nodes #edges DescriptionProject 37907
(26%)804856(0.11%)
Nodes are teachers (eTwinners) and there is a connection (edge) between two 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(75%)
573602(0.01%)
Nodes are teachers and there is an edge between two teachers if at least one teacher is in the contact list of the other. Edges are undirected and unweighted.
Project diary 3264(2.2%)
3436(0.06%)
Nodes are teachers and there is an edge between two teachers if one teacher has commented on at least one blog post created by the other. Edges are directed and weighted by the number of comments.
My journal 23919(16%)
30048(0.01%)
Nodes are teachers and there is an edge between two teachers if one teacher has 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 Projects are considered as groups to study group social capital
TeLLNet
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. JarkeI5-PCPK-0412-11
Properties of Teacher Networks:The Power Law Degree Distribution
0 2 4 6 8 10
8
6
4
2
0
2
4
Cum
ulat
ive
Freq
uenc
y
Degree
Project network degree distribution
Raw data
y=28.209x 1.455
0 2 4 6 8 10 10
8
6
4
2
0
2
4
6
Cum
ulat
ive
Freq
uenc
y
Degree
Contact network degree distribution
Raw data
y=327.630x 1.933
0 1 2 3 4 5 6 8
6
4
2
0
2
4
6
Cum
ulat
ive
Freq
uenc
y
Degree
Project diary network degree distribution
Raw data
y=14.904x 1.625
0 1 2 3 4 5 6 7 8
6
4
2
0
2
4
6
Cum
ulat
ive
Freq
uenc
y
Degree
My journal network degree distribution
Raw data
y=38.875x 1.750
Degree distribution of eTwinning networks follow the power law with the formula axy
TeLLNet
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. JarkeI5-PCPK-0412-12
Social Capital of Teachers
0 10 20 30 40 50 60 700
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Freq
uenc
y
Number of quality labels
(a) Quality labels and number of projects/posts/contacts/wall posts
Project diaryContactProjectMy journal
0 10 20 30 40 50 60 700
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Degr
ee
Number of quality labels
(b) Quality labels and degree
Project diaryContactProjectMy journal
0 10 20 30 40 50 60 700
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Betw
eenn
ess
Number of quality labels
(c) Quality labels and betweenness
Project diaryContactProjectMy journal
0 10 20 30 40 50 60 700
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Clus
terin
g
Number of quality labels
(d) Quality labels and clustering
Project diaryContactProjectMy journal
Structural hole as a form of social capital in eTwinning networks
TeLLNet
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. JarkeI5-PCPK-0412-13
Projects Achievement and Non-structural Properties
0 5 10 15 20 25 30 35 400
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1Fr
actio
n of
rece
ived
qua
lity
labe
l pro
ject
s
Number of countries involved, languages used and subjects
Quality label of projects and their properties
0 10 20 30 40 50 60 70 800
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Frac
tion
of re
ceiv
ed q
ualit
y la
bel p
roje
cts
Number of teachers and institution involved
Quality label of projects and their properties
CountryLanguageSubject
TeacherInstitution
Number of countries and languages used somehow correlate to the quality Number of teachers and institutions: effect on small projects (less than 30 members) Subject has no effect
TeLLNet
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. JarkeI5-PCPK-0412-14
Projects Achievement and Structural Properties
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Frac
tion
of re
ceiv
ed q
ualit
y la
bel p
roje
cts
Network parameters
Quality label of projects and their members network parameters
DensityClustering coefficientMaximum betweennessLargest component
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
Form of social capital: structural hole
TeLLNet
Lehrstuhl Informatik 5(Information Systems)
Prof. Dr. M. JarkeI5-PCPK-0412-15
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