Acis sna-seminar-0412-cao

TeLLNet

Learning Analytics
- Social Network Analysis for
Learning Communities
Yiwei Cao
RWTH Aachen University
Advanced Community Information Systems (ACIS)
cao@dbis.rwth-aachen.de
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
I5-Cao-0412-1 This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

Advanced Community Information
Systems (ACIS)

TeLLNet

Web Engineering
Responsive
Community

Web Analytics
Open
Visualization
Community
and
Information
Simulation
Systems

Community Community
Support Analytics

Requirements
Prof. Dr. M. Jarke
I5-Cao-0412-2
Engineering

Advanced
Community Information Systems
• LAS & Services • yFiles
TeLLNet • youTell • Repast
• SeViAnno • AERCS

Responsive • Network
• Advanced Community Models
Open
Web & Visualization
Community • Network
Multimedia & Simulation
Environments Analysis
Technologies
Web Engineering

• Actor Network

Web Analytics
• XMPP Theory
• HTML5 • Communities of
• MPEG-7 Community Community Practice
• Web Support Analytics • Game Theory
Services • Community
• Requirements • MediaBase Detection
• RESTful Bazaar • Web Mining
• PALADIN
• LAS • CAMRS • MobSOS • Recommender
• Cloud Systems
Computing • Multi Agent
• Mobile Simulation
Computing
Social Requirements Engineering

• Agent and Goal Oriented i* Modeling
(Information Systems) • Participatory Community Design
Prof. Dr. M. Jarke
I5-Cao-0412-3

Agenda

TeLLNet
Learning analytics
Social network analysis (SNA)
Case study
– TeLLNet for eTwinning & CAfe
– AERCS for the computer researcher community
– TEL-Map Learning Frontiers Dashboard
Demonstration of the prototypes
Conclusions and discussions
Prof. Dr. M. Jarke
I5-Cao-0412-4

TeLLNet

Learning Analytics

Prof. Dr. M. Jarke
I5-Cao-0412-5

Learning Analytics for
Self-Regulated Learning

TeLLNet

The Horizon Report – 2011 Edition

Prof. Dr. M. Jarke
I5-Cao-0412-6 Based on (Fruhmann, Nussbaumer, Albert, 2010)

Learning Analytics Support
Interdisciplinary multidimensional model of learning networks
TeLLNet
– Social network analysis (SNA) is defining measures for social relations
– i* Framework is defining learning goals and dependencies in
self-regulated learning CoP
– Learning Analytics & Visualization for CoP

social software Media Networks network of artifacts
Wiki, Blog, Podcast, IM, Chat, Microcontent, Blog entry, Message, Burst, Thread,
Email, Newsgroup, Chat … Comment, Conversation, Feedback (Rating)

i*-Dependencies
(Structural, Cross-media)

network of members

Members
(Social Network Analysis: Centrality,
Efficiency)
Prof. Dr. M. Jarke Communities of practice
I5-Cao-0412-7

Learning Analytics

TeLLNet
Data Visual Context Network Learning
analysis analytics analysis analysis analytics

Data analysis is a process of inspecting, cleaning, transforming, and modeling data in
order to highlight useful information, to suggest conclusions, and to support decision
making (Wikipedia)
Visual analytics analytical reasoning facilitated by interactive visual interfaces (Wong
& Thomas, 2004)
Context analysis is a method to analyze the environment in which a business
operates (Wikipedia), here: the learning business
Network analysis basis of network science, including SNA, link analysis, etc.
Learning analytics is the solution for large scale network
Prof. Dr. M. Jarke
I5-Cao-0412-8

Data Analysis

TeLLNet
The mass of data
Cleaning
Modeling
Management
Cross-disciplinary
Cross-media
Cross-platform

Prof. Dr. M. Jarke
I5-Cao-0412-9

Visual Analytics
A video of a Tang poem as a Learning resource

TeLLNet Tang poem - Jingyesi

Macro level
annotation

Semantic annotation

Learner community

Lehrstuhl Informatik 5 Meso level annotation Context annotation (location)
Prof. Dr. M. Jarke Micro level annotation
I5-Cao-0412-10

Context Analytics

TeLLNet
SWOT analysis
– Internal vs. external
– Based on questionnaires, interviews, expert opinions, pilot
study, feedback, etc.
Trend analysis
– Prediction techniques
Competence analysis
– Competence modeling
– Competence management
Prof. Dr. M. Jarke
Content analysis
I5-Cao-0412-11

Competence: Social Capital
Human capital vs. social capital (Burt, 1992)
TeLLNet
– 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)

Prof. Dr. M. Jarke
I5-Cao-0412-12

Network Analysis

TeLLNet

A community development model (Pham et al., 2011)

In which stage is the members’ network of a given group?
Prof. Dr. M. Jarke
I5-Cao-0412-13
How does it relate to the performance of the group?

TeLLNet

Social Network Analysis

Prof. Dr. M. Jarke
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Centrality
Given the network G=(V,E), where V is the set of nodes and E is the
TeLLNet
set of edges
Betweenness
σ u (i, j )
B (u ) = ∑
u ≠ i ≠ j σ (i, j )

where: σ u ( i , j ): number of shortest paths between nodes i and j that pass through
node u
σ ( i , j ): total number of shortest paths between nodes i and j
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
Prof. Dr. M. Jarke
I5-Cao-0412-15

Qualify the Stage of Network
Density: fraction of actual edges in the network
TeLLNet
{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
- Diameter: the longest shortest path between any pair of nodes
Prof. Dr. M. Jarke - Average shortest path length
I5-Cao-0412-16

Network Characteristics:
Connectivity & Degree distribution
Connectivity: measured by degree
TeLLNet
Degree zi ≡ N i = { j ∈ N : ij ∈ L} ,
i
where N 1 is first(-order) neighbor

Second-order neighbor, where “geodesic” distance = 2
i i
N2 ≡ { j ∈ N {i} : ∃k ∈ N , s.t.ik ∈ L ∧ kj ∈ L} N 1
Second-order degree:
z ≡N
i
2
i
2

Prof. Dr. M. Jarke
I5-Cao-0412-17

Important Types of
Degree Distribution
For any network Γ, its (kth-order) degree distribution
TeLLNet
p(·) specifies 1
p(k ) = {i ∈ N : zi = k} for each k = 0,
n 1, …, n-1
Binomial distribution with density
Poisson distribution with density
Geometric distribution with density
Power-law distribution with density

Prof. Dr. M. Jarke
I5-Cao-0412-18

Power-Law Distribution
−γ
TeLLNet p(k ) = Ak (k = 1, 2, ... )

Here: A = 1 / R (γ )
∞
where R (γ ) ≡ ∑k −γ
is the Riemann Zeta function
k =1 and normalizes the distribution
This degree distribution is scale-free if
−γ
p(αk ) = α p(k ) For any α and k
Prof. Dr. M. Jarke
I5-Cao-0412-19

From Small World Model to
Scale-Free Networks
The “small world” proposed by Watts and Strogatz
TeLLNet
– Reconciles local structure (high clustering)
– Presents typical internode proximity (low average distances)
– Does not account for the heterogeneity of many real-world networks
– Does not accommodate diversity of social networks due to low
values of the “rewiring probability”
Barabási and Albert embodies an explicit dynamic process of
network formation with
– Growth: the network is formed through the successive arrival of
new nodes that, upon entry, link to some of the preexisting nodes
– Preferential attachment: the (stochastic) mechanism used by
new nodes in establishing their links is biased in favor of those that
Lehrstuhl Informatik 5 are more highly connected at the time of their entrance
Prof. Dr. M. Jarke
I5-Cao-0412-20

Forming Networks
Considering Growth Alone

TeLLNet
Considering growth alone
– Growing set of nodes
– Unbiased linking
Growth along can not be the only factor for network
evolvement
– If random linking is unbiased, the induced networks
display a geometric degree distribution ( so-called
exponential networks)
– They are not qualitatively very different from the Poisson
networks obtained in a stationary context
Prof. Dr. M. Jarke
I5-Cao-0412-21

Scale-Free Networks
Scale-free networks are in the sense that the degree distribution is
TeLLNet
power-law distributed: P(k ) ∝ k −γ

The degree distribution is scale invariant only if the preferential
attachment rule is perfectly linear; otherwise the degree is distributed
according to a stretched exponential function
The diameter of Barabási-Albert networks [Bollobás & Riordan, 2004]
ˆ
d ∝ ln(n) / ln(ln(n))
The clustering coefficient of a Barabási-Albert model is five times larger
than those of a random graph with comparable size and order. It
decreases with the network order
Prof. Dr. M. Jarke
I5-Cao-0412-22

The Small World Model
In The Real World
Clustering coefficient C
TeLLNet
Network n z measured Random graph
Internet 6374 3.8 0.24 0.00060
World Wide Web 153127 35.2 0.11 0.00023
Power grid 4941 2.7 0.080 0.00054
Biology collaborations 1520251 15.5 0.081 0.000010
Mathematics collaborations 253339 3.9 0.15 0.000015
Film actor collaborations 449913 113.4 0.20 0.00025
Company directors 7673 14.4 0.59 0.0019
Word cooccurrence 460902 70.1 0.44 0.00015
Neural network 282 14.0 0.28 0.049
Metabolic network 315 28.3 0.59 0.090
Food web 134 8.7 0.22 0.065
Prof. Dr. M. Jarke
I5-Cao-0412-23
[Newman et al., 2006]

Social Capital:
Structural Hole vs. Closure
Structural holes (Burt, 1992)
TeLLNet - 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
(Information Systems) - Network structural property: either structural hole or closure
Prof. Dr. M. Jarke
I5-Cao-0412-24

Identification of Individual
Social Capital
Given the network G=(V,E), where V is the set of nodes and E is the
TeLLNet
set of edges
Structural holes: nodes with high betweenness
σ u (i, j )
B (u ) = ∑
u ≠ i ≠ j σ (i, j )

where: σ u ( i , j ): number of shortest paths between nodes i and j that pass through
node u
σ ( 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
Prof. Dr. M. Jarke
I5-Cao-0412-25

Reading List to
Social Network Analysis
Social Network Analysis: Methods and Applications by Stanley
TeLLNet
Wasserman, Katherine Faust, Dawn Lacobucci
Models and Methods in Social Network Analysis by Peter J. Carrington,
John Scott, Stanley Wasserman
Social Network Analysis: A Handbook by John P Scott
Introducing Social Networks by Alain Degenne, Michel Forse
The Development of Social Network Analysis: A Study in the Sociology
of Science by Linton C. Freeman

A longer reading list is at
http://beamtenherrschaft.blogspot.com/2008/10/
social-network-analysis-and-complexity.html
Prof. Dr. M. Jarke
I5-Cao-0412-26 Lecture at RWTH Aachen University: Web Science

TeLLNet

Case Study I
TeLLNet for eTwinning
(Breuer et al., EC-TEL 2009, Song et al., EC-TEL 2011,
Pham et al., NLC 2012)

Prof. Dr. M. Jarke
I5-Cao-0412-27

TeLLNet - SNA for European
Teachers’ Lifelong Learning
How to manage and handle
TeLLNet
large scale data on social
networks?
How to analyse social network
data in order to develop
teachers’ competence, e.g. to
facilitate a better project
collaboration?
How to make the network
visualization useful for teachers’
lifelong learning?

Prof. Dr. M. Jarke
I5-Cao-0412-28

Data Set
Data #data entries Description
Project 23641 Schools from at least two schools from at least two different European countries create a
TeLLNet 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
Prof. Dr. M. Jarke
Statistics on eTwinning data (as of 11.11.2011)
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eTwinning Network
Network #nodes #edges Description
Project 37907 804856 Nodes are teachers (eTwinners) and there is a connection (edge) between two
TeLLNet (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
(Information Systems) Projects are considered as groups to study group social capital
Prof. Dr. M. Jarke
I5-Cao-0412-30

eTwinning
Network Information Visualization

TeLLNet

• Teacher network 2008 as example
•Cooperation among countries
Prof. Dr. M. Jarke
I5-Cao-0412-31

Analysis and Visualization of
Lifelong Learner Data
Performance Data on Projects Network Structures and Patterns
TeLLNet

Prof. Dr. M. Jarke
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System Architecture of
Prototype CAfe

TeLLNet

Prof. Dr. M. Jarke
I5-Cao-0412-33

Self-monitoring of Teacher Network
in CAfe
Target users
TeLLNet – European teachers (teachers‘ workshops)
– Administrators & policy-makers

Prof. Dr. M. Jarke
I5-Cao-0412-34

Self-Monitoring of Competence
Management

TeLLNet

Prof. Dr. M. Jarke
I5-Cao-0412-35

Self-Monitoring of Competence
Management
Community level ->
TeLLNet

Teacher level

Prof. Dr. M. Jarke
I5-Cao-0412-36

Properties of Teacher Networks:
The Power Law Degree Distribution

TeLLNet

−α
Prof. Dr. M. Jarke
Degree distribution of eTwinning networks follow the power law with the formula y = ax
I5-Cao-0412-37

Teachers’ Social Capital

TeLLNet

Prof. Dr. M. Jarke
I5-Cao-0412-38
Structural hole as a form of social capital in eTwinning networks

Projects Achievement and
Non-structural Properties

TeLLNet

Number of countries and languages used somehow correlate to the quality
Number of teachers and institutions: effect on small projects (less than 30 members)
(Information Systems) Subject has no effect
Prof. Dr. M. Jarke
I5-Cao-0412-39

Projects Achievement and
Structural Properties

TeLLNet

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
Prof. Dr. M. Jarke Form of social capital: structural hole
I5-Cao-0412-40

Summary
SNA & visualization as tools for Social capital in eTwinning
TeLLNet
competence development in Network
learning networks – Both teachers and projects follow
– Competence assessment is still structural hole
limited in performance indication – The informational diversity is the
Social capital defined in key success factor
eTwinning Network Applications: recommendation
– By SNA metrics tools
– By a development model – Help teachers find projects,
– Network structure of projects and contacts, etc.
position of teachers: identified via – Help project organizers find, select
networks created by several and invite project partners
communication mechanisms (e.g.
message, project collaboration,
Prof. Dr. M. Jarke
blog)
I5-Cao-0412-41

TeLLNet

Case Study II
AERCS for Computer Scientist
Community
(Klamma et al., Complex 2009; Pham et al., ASONAM 2010; Pham et al. ???)

Prof. Dr. M. Jarke
I5-Cao-0412-42

Data Set
DBLP (http://www.informatik.uni-trier.de/~ley/db/)
TeLLNet
- 788,259 author’s names
- 1,226,412 publications
- 3,490 venues (conferences, workshops, journals)
CiteSeerX (http://citeseerx.ist.psu.edu/)
- 7,385,652 publications
- 22,735,240 citations
- Over 4 million author’s names
Combination
- Canopy clustering (McCallum, 2000)
- Result: 864,097 matched pairs
- On average: venues cite 2306 and are cited 2037 times
Prof. Dr. M. Jarke
I5-Cao-0412-43

AERCS - Recommendation of Venues
for Young Computer Scientists

TeLLNet

Prof. Dr. M. Jarke
I5-Cao-0412-44

TeLLNet

Knowledge Network
at Cluster Level

(Information Systems) (Pham, Klamma, Jarke: Development of Computer Science
Prof. Dr. M. Jarke Disciplines – A Social Network Analysis Approach, SNAM, 2011)
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Interdisciplinary Series:
Top Betweenness Centrality

TeLLNet

Prof. Dr. M. Jarke
I5-Cao-0412-46

High Prestige Series:
Top PageRank

TeLLNet

Prof. Dr. M. Jarke
I5-Cao-0412-47

Academic Community Development
Development of the
TeLLNet community: number of
participants over years

Continuity: participants by
number of events attended

Prof. Dr. M. Jarke
I5-Cao-0412-48
ACM SIGMOD

Dynamic Networks:
The VLDB Community

TeLLNet

VLDB 1990 VLDB 1995

Prof. Dr. M. Jarke VLDB 2000 VLDB 2006
I5-Cao-0412-49

Learning Analytics: EC-TEL
Community among TEL Communities
ICALT, ICWL, EC-TEL, IST, AIED (Pham, Derntl & Klamma, 2011)
TeLLNet

Prof. Dr. M. Jarke
I5-Cao-0412-50

Community Visualizer for ICWL

TeLLNet

Prof. Dr. M. Jarke
I5-Cao-0412-51

Summary
Series in computer science
TeLLNet - Tend to be focused: developed main theme as core topic
- Not so many series is successful in motivating authors to work on the main theme
Conferences vs. journals
- The same trend in the development of main topics
- Conferences facilitate communication between participants: authors tend to
collaborate cross communities
Next questions:
- How do series develop over time?
- Can we detect the development patterns?
- Can we identify good or bad development behavior?
Applications:
- To create awareness for conference/journal organizers and stakeholders
Prof. Dr. M. Jarke
- To give an overview of the community to researchers
I5-Cao-0412-52

TeLLNet

Case Study III
Tel-Map
(Derntl et al.: Mapping the European TEL Project Landscape Using Social
Network Analysis and Advanced Query Visualization, ADVTEL 2011)

Prof. Dr. M. Jarke
I5-Cao-0412-53

Work context

TeLLNet

Mapping and roadmapping for TEL
Understanding the current TEL landscape
Finding strong and weak signals for change at different
levels
Different methods, e.g. Delphi, Community modeling,
Text analysis, Visual analytics, etc.
Here: Social network analysis and visualization
Prof. Dr. M. Jarke
I5-Cao-0412-54

Data Set
Progr. Call # Projects (acronyms)
Call 2005 4 CITER, JEM, MACE, MELT
TeLLNet
Call 2006 7 COSMOS, EdReNe, EUROGENE, eVip, Intergeo, KeyToNature, Organic.Edunet
ECP
Call 2007 3 ASPECT, iCOPER, EduTubePlus
Call 2008 5 LiLa, Math-Bridge, mEducator, OpenScienceResources, OpenScout
IST-2002- CONNECT, E-LEGI, ICLASS, KALEIDOSCOPE, LEACTIVEMATH, PROLEARN,
8
2.3.1.12a TELCERT, UNFOLD
IST-2004- APOSDLE, ARGUNAUT, ATGENTIVE, COOPER, ECIRCUS, ELEKTRA, I-MAESTRO,
FP6 2.4.10b
14
KP-LAB, L2C, LEAD, PALETTE, PROLIX, RE.MATH, TENCOMPETENCE
IST-2004- ARISE, CALIBRATE, ELU, EMAPPS.COM, ICAMP, LOGOS, LT4EL, MGBL, UNITE,
10
2.4.13c VEMUS
ICT-2007.4.1d 6 80DAYS, GRAPPLE, IDSPACE, LTFLL, MATURE, SCY
ICT-2007.4.3d 7 COSPATIAL, DYNALEARN, INTELLEO, ROLE, STELLAR, TARGET, XDELIA
FP7
ALICE, ARISTOTELE, ECUTE, GALA, IMREAL, ITEC, METAFORA, MIROR,
ICT-2009.4.2b 13
MIRROR, NEXT-TELL, SIREN, TEL-MAP, TERENCE

Total: 77
(Information Systems) a … Technology-enhanced learning and access to cultural heritage” c … Strengthening the Integration of the ICT research effort in an Enlarged Europe”
Prof. Dr. M. Jarke
I5-Cao-0412-55
b … Technology-Enhanced Learning d … Digital libraries and technology-enhanced learning”

Data set

TeLLNet

Prof. Dr. M. Jarke
I5-Cao-0412-56

TEL Projects as Social Networks

TeLLNet

Prof. Dr. M. Jarke
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GP – FP7 project progression

TeLLNet

Prof. Dr. M. Jarke
I5-Cao-0412-58

FP6, FP7,
eContentplus
FP6, FP7, ECP projects

TeLLNet

Central role of IPs and NoEs as
sources and harbors of
consortia
Prof. Dr. M. Jarke
eContentplus as “gap filler”
I5-Cao-0412-59

Project ranking
Local
Progr- Start Authority Page- Weighted Close. Betw.
TeLLNet
PROJECT Hub Degree Clust.
amme Year ▼ Rank Degree Centrality Centrality
Coeff.

GALA FP7 2010 .0546 [1] .0634 [1] .0338 [4] 42 [3] 79 [5] .6847 [4] .0585 [3] .3449 [74]

OpenScout ECP 2009 .0442 [2] .0495 [2] .0287 [6] 37 [6] 72 [6] .6609 [6] .0310 [7] .4790 [56]

TEL-MAP FP7 2010 .0416 [3] .0000 [75] .0207 [11] 31 [11] 51 [11] .6230 [11] .0147 [20] .5032 [50]

STELLAR FP7 2009 .0403 [4] .0464 [3] .0324 [5] 42 [3] 81 [4] .6909 [3] .0390 [4] .4135 [71]

ROLE FP7 2009 .0338 [5] .0386 [4] .0252 [8] 36 [7] 61 [8] .6552 [7] .0347 [6] .4540 [63]

iCOPER ECP 2008 .0338 [5] .0386 [4] .0354 [3] 39 [5] 91 [3] .6667 [5] .0224 [12] .4764 [59]

Math-Bridge ECP 2009 .0299 [7] .0340 [6] .0156 [18] 26 [15] 35 [17] .5891 [16] .0163 [15] .5446 [42]

ASPECT ECP 2008 .0286 [8] .0309 [7] .0250 [9] 30 [12] 59 [9] .6179 [12] .0289 [8] .4989 [55]

mEducator ECP 2009 .0260 [9] .0294 [8] .0135 [23] 24 [18] 28 [26] .5891 [16] .0234 [10] .5580 [40]

ITEC FP7 2010 .0260 [9] .0294 [8] .0167 [16] 22 [22] 37 [16] .5758 [23] .0176 [14] .5022 [51]

MIRROR FP7 2010 .0260 [9] .0294 [8] .0129 [25] 24 [18] 29 [23] .5802 [20] .0061 [30] .6051 [33]

Prof. Dr. M. Jarke
I5-Cao-0412-60

Geo-mapping
http://is.gd/fp7telmap
TeLLNet

Prof. Dr. M. Jarke
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GO – Project Collaborations FP7
Each project creates ties among its consortium members
TeLLNet

Prof. Dr. M. Jarke
I5-Cao-0412-62

Project collaborations

TeLLNet

Prof. Dr. M. Jarke
I5-Cao-0412-63

Top Collaborators in FP7
Technische Universität Graz, Austria (82 conn. in 7 projects)
TeLLNet Open Universiteit Nederland, Netherlands (67 / 5)
Aalto-Korkeakoulusaatio, Finland (66 / 3)
Katholieke Universiteit Leuven, Belgium (63 / 4).
ATOS Origin Sociedad Anonima Espanola, Spain (59 / 4)

Prof. Dr. M. Jarke
I5-Cao-0412-64

Project Collaborations FP6,7, ECP
605 organizations
TeLLNet in 77 projects
creating 9K+
collaboration ties

Prof. Dr. M. Jarke
I5-Cao-0412-65

Top 10 Organizations
Organization PR ▼ BC LC DC CC Funding*
TeLLNet THE OPEN UNIVERSITY .0125 [1] .1209 [1] .2135 [603] 220 [1] .5421 [1] 3.55 [3]
KATHOLIEKE UNIVERSITEIT LEUVEN .0090 [2] .0770 [2] .1701 [605] 149 [3] .5628 [5] 2.56 [5]
OPEN UNIVERSITEIT NEDERLAND .0085 [3] .0411 [5] .2159 [602] 133 [7] .6014 [6] 3.45 [4]
JYVASKYLAN YLIOPISTO .0080 [4] .0667 [3] .3168 [590] 170 [2] .5480 [2] 1.26 [39]
DEUTSCHES FORSCHUNGSZENTRUM FUER
.0066 [5] .0409 [6] .1892 [604] 107 [25] .5550 [17] 3.68 [1]
KUENSTLICHE INTELLIGENZ GMBH
ATOS ORIGIN SOCIEDAD ANONIMA ESPANOLA .0064 [6] .0237 [15] .4316 [565] 142 [5] .5335 [4] 1.33 [33]
UNIVERSITAET GRAZ .0064 [7] .0229 [18] .4016 [574] 148 [4] .5279 [3] 2.03 [10]
UNIVERSITEIT UTRECHT .0061 [8] .0204 [23] .4323 [564] 139 [6] .5279 [11] 1.62 [19]
INESC ID - INSTITUTO DE ENGENHARIA DE
SISTEMAS E COMPUTADORES, INVESTIGACAO E .0061 [9] .0368 [7] .4741 [550] 130 [8] .5261 [19] 1.68 [16]
DESENVOLVIMENTO EM LISBOA
THE UNIVERSITY OF WARWICK .0058 [10] .0329 [8] .4754 [549] 129 [10] .5025 [10] 1.68 [17]

PR = PageRank | BC = Betweenness centrality | LC = Local clustering coefficient | DC = Degree centrality | CC = Closeness centrality

Prof. Dr. M. Jarke
I5-Cao-0412-66

Two Clustering Spheres
Connectedness of
TeLLNet the neighborhood

137 / 605 (23%) are
on the “higher
sphere”.

KALEIDOSCOPE (100%), STELLAR (94%), PROLEARN (91%),
RE.MATH (88%), GRAPPLE, ALICE, TEL-Map (80% each), ICOPER
Prof. Dr. M. Jarke
I5-Cao-0412-67 (74%) and IMREAL (72%).

Top partnership bonds
Organizational pairing, e.g. OUNL + Hannover, OU +
TeLLNet
KUL (6), OU + OUNL / IMC / JYU (5), …
The most important projects where the 22 strongest
partnership pairs (4 or more projects) participated:
1. PROLEARN (FP6; 16 pairs),
2. ICOPER (eContentplus; 10 pairs),
3. OpenScout (eContentplus; 9 pairs),
4. GRAPPLE (FP7; 8 pairs),
5. STELLAR, ROLE (FP7; 5 pairs), and
Lehrstuhl Informatik 5 7. PROLIX (FP6, 5 pairs)
Prof. Dr. M. Jarke
I5-Cao-0412-68

Want to Explore?

TeLLNet

Prof. Dr. M. Jarke
I5-Cao-0412-69

Summary
IPs and NoEs and large ECP consortia are most central
TeLLNet
projects (also: partnership bonds, clustering)
“Multicultural” list of top organizations
ECP as incubator for FP7 projects; strengthened weak ties.
Research follows money.
Two classes: clustered/loose neighborhood. Some achieve a
clustering-paribus increase in SNA metrics
Fresh blood is draining; bonds are growing stronger. We’re a
family.
SNA is capable of revealing clusters of organizations and
projects that can be used as indicators of impact and
Prof. Dr. M. Jarke
I5-Cao-0412-70 sustainability

Demonstrations
eTwinning CAfe
TeLLNet

AERCS:
http://bosch.informatik.rwth-aachen.de:5080/AERCS/

Learning Frontiers Dashboard
http://learningfrontiers.eu/?q=dashboard#
Prof. Dr. M. Jarke
I5-Cao-0412-71

Conclusions
Informal learning needs support of learning analytics
TeLLNet
SNA is very useful for knowledge discovery
Detection the development pattern of learner communities
supports context analytics and visual analytics
User interface design influences visual analytics

Prof. Dr. M. Jarke
I5-Cao-0412-72

Interdisciplinary Discussions

TeLLNet
Learning analytics or just data mining in TEL?
What are the roles of learner communities in learning
analytics?
How do communities of practice work in learning
networks?

Prof. Dr. M. Jarke
I5-Cao-0412-73

Acis sna-seminar-0412-cao

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