Acis sna-seminar-0412-cao

TeLLNet

Lehrstuhl Informatik 5(Information Systems)

Prof. Dr. M. JarkeI5-Cao-0412-1 This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

Learning Analytics - Social Network Analysis for

Learning Communities

Yiwei CaoRWTH Aachen University

Advanced Community Information Systems (ACIS)[email protected]

TeLLNet


Prof. Dr. M. JarkeI5-Cao-0412-2

Responsive Open

Community Information

Systems

Community Visualization

and Simulation

Community Analytics

Community Support

WebAnalytics

Web

Eng

inee

ring

Advanced Community Information Systems (ACIS)

RequirementsEngineering

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AdvancedCommunity Information Systems

• Network Models

• Network Analysis

• Actor Network Theory

• Communities ofPractice

• Game Theory• Community

Detection• Web Mining• Recommender

Systems• Multi Agent

Simulation

Web

Ana

lytics

• AdvancedWeb & Multimedia Technologies• XMPP• HTML5• MPEG-7

• Web Services• RESTful• LAS

• CloudComputing

• Mobile Computing

Web

Eng

ineer

ing

• MediaBase• PALADIN• MobSOS

• RequirementsBazaar

• CAMRS

• yFiles• Repast• AERCS

• LAS & Services• youTell• SeViAnno

ResponsiveOpen

Community Environments

Community Visualization& Simulation

Community Analytics

Community Support

Social Requirements Engineering

• Agent and Goal Oriented i* Modeling• Participatory Community Design

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Agenda

Learning analyticsSocial network analysis (SNA)Case study– TeLLNet for eTwinning & CAfe– AERCS for the computer researcher community– TEL-Map Learning Frontiers Dashboard

Demonstration of the prototypesConclusions and discussions

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Learning Analytics

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Learning Analytics for Self-Regulated Learning

Based on (Fruhmann, Nussbaumer, Albert, 2010)

The Horizon Report – 2011 Edition

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Learning Analytics SupportInterdisciplinary multidimensional model of learning networks– 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 softwareWiki, Blog, Podcast, IM, Chat, Email, Newsgroup, Chat …

i*-Dependencies(Structural, Cross-media)

Members(Social Network Analysis: Centrality,

Efficiency)

network of artifactsMicrocontent, Blog entry, Message, Burst, Thread,

Comment, Conversation, Feedback (Rating)

network of members

Communities of practice

Media Networks

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Learning Analytics

Data analysis

Visual analytics

Contextanalysis

Network analysis

Learning 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 businessNetwork analysis basis of network science, including SNA, link analysis, etc.Learning analytics is the solution for large scale network

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Data Analysis

The mass of dataCleaningModelingManagementCross-disciplinary Cross-mediaCross-platform

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Visual Analytics

Macro levelannotation

Meso level annotation

A video of a Tang poem as a Learning resource

Semantic annotation

Context annotation (location)

Learner community

Micro level annotation

Tang poem - Jingyesi

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Context Analytics

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

Content analysis

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Competence: Social Capital 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)

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Network Analysis

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)

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Social Network Analysis

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CentralityGiven the network G=(V,E), where V is the set of nodes and E is the set of edgesBetweenness

where: : number of shortest paths between nodes i and j that pass through node u

: total number of shortest paths between nodes i and jLocal clustering coefficient

where: is the set of neighbors of node u

∑≠≠

=jiu

u

jijiuB),(),()(

σσ

),( jiuσ

),( jiσ

{ }( )( ) 2/1)(N(u)

E w)(v, :N(u) wv,C(u)

−∈∈

=uN

)( uN

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Qualify the Stage of NetworkDensity: fraction of actual edges in the network

, n is the number of nodes

Global clustering coefficient

Maximum betweenness: highest betweenness of nodesLargest connected component: fraction of nodes in largest connected componentFor large member networks- Diameter: the longest shortest path between any pair of nodes- Average shortest path length

triplesconnected ofnumber trianglesofnumber 3×

=D

{ }n2

E w)(v, :V wv,D

∈∈=

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Network Characteristics: Connectivity & Degree distributionConnectivity: measured by degreeDegree , where is first(-order) neighbor

Second-order neighbor, where “geodesic” distance = 2

Second-order degree:

}:{ LijNjz ii ∈∈=≡ Ni1N

ii LkjLiktsNkiNj 12 \}..,:}{\{ NN ∈∧∈∈∃∈≡

i2N≡iz2

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Important Types of Degree Distribution

For any network Γ, its (kth-order) degree distribution p(·) specifies for each k = 0,

1, …, n-1Binomial distribution with density Poisson distribution with densityGeometric distribution with densityPower-law distribution with density

}:{1)( kzNin

kp i =∈=

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Power-Law Distribution

)(/1 γR=A

∑∞

=

−≡1

)(k

k γγR

γ−= Akkp )( (k = 1, 2, ... )

Here:

where is the Riemann Zeta functionand normalizes the distribution

This degree distribution is scale-free if

)()( kpkp γαα −= For any α and k

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From Small World Model to Scale-Free Networks

The “small world” proposed by Watts and Strogatz – 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 are more highly connected at the time of their entrance

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Forming Networks Considering Growth Alone

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

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Scale-Free Networks Scale-free networks are in the sense that the degree distribution is power-law distributed:

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 functionThe diameter of Barabási-Albert networks [Bollobás & Riordan, 2004]

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

γ−∝ kkP )(

))ln(ln(/)ln(ˆ nnd ∝

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The Small World Model In The Real World

Clustering coefficient CNetwork n z measured Random graphInternet 6374 3.8 0.24 0.00060World Wide Web 153127 35.2 0.11 0.00023Power grid 4941 2.7 0.080 0.00054Biology collaborations 1520251 15.5 0.081 0.000010Mathematics collaborations 253339 3.9 0.15 0.000015Film actor collaborations 449913 113.4 0.20 0.00025Company directors 7673 14.4 0.59 0.0019Word cooccurrence 460902 70.1 0.44 0.00015Neural network 282 14.0 0.28 0.049Metabolic network 315 28.3 0.59 0.090Food web 134 8.7 0.22 0.065

[Newman et al., 2006]

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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

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Identification of Individual Social Capital

Given the network G=(V,E), where V is the set of nodes and E is the set of edgesStructural 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 jClosures: nodes with high local clustering coefficient

where: is the set of neighbors of node u

∑≠≠

=jiu

u

jijiuB),(),()(

σσ

),( jiuσ

),( jiσ

{ }( )( ) 2/1)(N(u)

E w)(v, :N(u) wv,C(u)

−∈∈

=uN

)( uN

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Reading List to Social Network Analysis

Social Network Analysis: Methods and Applications by Stanley Wasserman, Katherine Faust, Dawn LacobucciModels and Methods in Social Network Analysis by Peter J. Carrington, John Scott, Stanley WassermanSocial Network Analysis: A Handbook by John P ScottIntroducing Social Networks by Alain Degenne, Michel ForseThe Development of Social Network Analysis: A Study in the Sociology of Science by Linton C. Freeman

Lecture at RWTH Aachen University: Web Science

A longer reading list is athttp://beamtenherrschaft.blogspot.com/2008/10/

social-network-analysis-and-complexity.html

http://beamtenherrschaft.blogspot.com/2008/10/social-network-analysis-and-complexity.html

http://beamtenherrschaft.blogspot.com/2008/10/social-network-analysis-and-complexity.html

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Case Study I TeLLNet for eTwinning

(Breuer et al., EC-TEL 2009, Song et al., EC-TEL 2011, Pham et al., NLC 2012)

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TeLLNet - SNA for European Teachers’ Lifelong Learning

How to manage and handle 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?

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Data SetData #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)

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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 analysisNetwork parameters are computed using Oracle store proceduresProjects are considered as groups to study group social capital

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eTwinningNetwork Information Visualization

• Teacher network 2008 as example•Cooperation among countries

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Analysis and Visualization ofLifelong Learner Data

Performance Data on Projects Network Structures and Patterns

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System Architecture ofPrototype CAfe

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Self-monitoring of Teacher Network in CAfe

Target users– European teachers (teachers‘ workshops)– Administrators & policy-makers

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Self-Monitoring of Competence Management

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Community level ->

Teacher level

Self-Monitoring of Competence Management

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Properties of Teacher Networks:The Power Law Degree Distribution

Degree distribution of eTwinning networks follow the power law with the formula α−= axy

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Teachers’ Social Capital

Structural hole as a form of social capital in eTwinning networks

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Projects Achievement and Non-structural Properties

Number of countries and languages used somehow correlate to the qualityNumber of teachers and institutions: effect on small projects (less than 30 members)Subject has no effect

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Projects Achievement and Structural Properties

Project member networks: created using the previous project collaboration and wall messaging, reflect the early communication of project membersHigh quality projects prefer the Bonding stage: consists of seperated densely connected groups Form of social capital: structural hole

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SummarySNA & visualization as tools for competence development in learning networks– Competence assessment is still

limited in performance indicationSocial capital defined in eTwinning Network– By SNA metrics– By a development model– Network structure of projects and

position of teachers: identified via networks created by several communication mechanisms (e.g. message, project collaboration, blog)

Social capital in eTwinning Network– Both teachers and projects follow

structural hole– The informational diversity is the

key success factorApplications: recommendation tools– Help teachers find projects,

contacts, etc.– Help project organizers find, select

and invite project partners

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Case Study II AERCS for Computer Scientist

Community

(Klamma et al., Complex 2009; Pham et al., ASONAM 2010; Pham et al. ???)

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Data SetDBLP (http://www.informatik.uni-trier.de/~ley/db/)- 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

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AERCS - Recommendation of Venues for Young Computer Scientists

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(Pham, Klamma, Jarke: Development of Computer Science Disciplines – A Social Network Analysis Approach, SNAM, 2011)

Knowledge Network at Cluster Level

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Interdisciplinary Series:Top Betweenness Centrality

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High Prestige Series:Top PageRank

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Academic Community DevelopmentDevelopment of the community: number of participants over years

Continuity: participants by number of events attended

ACM SIGMOD

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Dynamic Networks:The VLDB Community

VLDB 1990 VLDB 1995

VLDB 2000 VLDB 2006

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Learning Analytics: EC-TEL Community among TEL Communities

ICALT, ICWL, EC-TEL, IST, AIED (Pham, Derntl & Klamma, 2011)

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Community Visualizer for ICWL

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SummarySeries in computer science- 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 communitiesNext 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- To give an overview of the community to researchers

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Case Study III Tel-Map

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

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Work context

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

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Data SetProgr. Call # Projects (acronyms)

ECP

Call 2005 4 CITER, JEM, MACE, MELT

Call 2006 7 COSMOS, EdReNe, EUROGENE, eVip, Intergeo, KeyToNature, Organic.Edunet

Call 2007 3 ASPECT, iCOPER, EduTubePlusCall 2008 5 LiLa, Math-Bridge, mEducator, OpenScienceResources, OpenScout

FP6

IST-2002-2.3.1.12a 8 CONNECT, E-LEGI, ICLASS, KALEIDOSCOPE, LEACTIVEMATH, PROLEARN,

TELCERT, UNFOLD

IST-2004-2.4.10b 14 APOSDLE, ARGUNAUT, ATGENTIVE, COOPER, ECIRCUS, ELEKTRA, I-MAESTRO,

KP-LAB, L2C, LEAD, PALETTE, PROLIX, RE.MATH, TENCOMPETENCE

IST-2004-2.4.13c 10 ARISE, CALIBRATE, ELU, EMAPPS.COM, ICAMP, LOGOS, LT4EL, MGBL, UNITE,

VEMUS

FP7

ICT-2007.4.1d 6 80DAYS, GRAPPLE, IDSPACE, LTFLL, MATURE, SCYICT-2007.4.3d 7 COSPATIAL, DYNALEARN, INTELLEO, ROLE, STELLAR, TARGET, XDELIA

ICT-2009.4.2b 13 ALICE, ARISTOTELE, ECUTE, GALA, IMREAL, ITEC, METAFORA, MIROR, MIRROR, NEXT-TELL, SIREN, TEL-MAP, TERENCE

Total: 77a … Technology-enhanced learning and access to cultural heritage”b … Technology-Enhanced Learning

c … Strengthening the Integration of the ICT research effort in an Enlarged Europe”d … Digital libraries and technology-enhanced learning”

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Data set

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TEL Projects as Social Networks

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GP – FP7 project progression

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FP6, FP7, ECP projectsFP6, FP7,

eContentplus

Central role of IPs and NoEs as sources and harbors of consortia

eContentplus as “gap filler”

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Project ranking

PROJECT Progr-amme

StartYear

Authority▼ Hub Page-

Rank Degree WeightedDegree

Close.Centrality

Betw.Centrality

LocalClust. 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]

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Geo-mappinghttp://is.gd/fp7telmap

http://is.gd/fp7telmap

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GO – Project Collaborations FP7Each project creates ties among its consortium members

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Project collaborations

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Top Collaborators in FP7Technische Universität Graz, Austria (82 conn. in 7 projects)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)

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605 organizationsin 77 projectscreating 9K+ collaboration ties

Project Collaborations FP6,7, ECP

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Top 10 OrganizationsOrganization PR ▼ BC LC DC CC Funding*

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 KUENSTLICHE INTELLIGENZ GMBH .0066[5] .0409[6] .1892[604] 107[25] .5550 [17] 3.68[1]

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 DESENVOLVIMENTO EM LISBOA

.0061[9] .0368[7] .4741[550] 130[8] .5261 [19] 1.68[16]

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

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Two Clustering Spheres

KALEIDOSCOPE (100%), STELLAR (94%), PROLEARN (91%), RE.MATH (88%), GRAPPLE, ALICE, TEL-Map (80% each), ICOPER (74%) and IMREAL (72%).

Connectedness of the neighborhood

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

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Top partnership bondsOrganizational pairing, e.g. OUNL + Hannover, OU + 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 7. PROLIX (FP6, 5 pairs)

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Want to Explore?

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SummaryIPs and NoEs and large ECP consortia are most central projects (also: partnership bonds, clustering)“Multicultural” list of top organizationsECP as incubator for FP7 projects; strengthened weak ties. Research follows money.Two classes: clustered/loose neighborhood. Some achieve a clustering-paribus increase in SNA metricsFresh 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 sustainability

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DemonstrationseTwinning CAfe

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

Learning Frontiers Dashboard http://learningfrontiers.eu/?q=dashboard#

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

http://learningfrontiers.eu/?q=dashboard

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ConclusionsInformal learning needs support of learning analyticsSNA is very useful for knowledge discovery Detection the development pattern of learner communities supports context analytics and visual analyticsUser interface design influences visual analytics

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Interdisciplinary Discussions

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?

Acis sna-seminar-0412-cao

Technology

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