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Ergang Song, Zinayida Petrushyna, Yiwei Cao, and Ralf KlammaInformation Systems and Databases, RWTH Aachen UniversityEC-TEL 2011Palermo, ItalySeptember 23, 2011
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TeLLNet
EC TEL 2011EC-TEL 2011
Learning Analytics at Large: th Lif l L i N t kthe Lifelong Learning Networkof 160, 000 European Teachers
Ergang Song, Zinayida Petrushyna, Yiwei Cao, and Ralf KlammaInformation Systems and Databases, RWTH Aachen University
Palermo, ItalySeptember 23 2011
Lehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-1
September 23, 2011
TeLLNet
MotivationsMotivations How to support lifelong learning (LLL)?
– New means for LLL with rapid development of ICT – Competence assessment methods for LLL in demand
S lf it i f LLL d d
(Meta-) Competence management
– Self-monitoring for LLL needed– Still lack of large data sets– Tools are needed instead of a concept
Self-monitoring
Tools are needed instead of a concept Case study: eTwinning Network
– Continuous professional development for teachers
Learning analyticsfor lifelong learning
p p– Aiming to promote collaborations among schools– Competence gap to recognize and to bridge– Meta-competence
Learning analytics is neededVi l l ti f lf it i
Lehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-2
– Visual analytics for self-monitoring– Multiple levels (individual, community, and network)
TeLLNet
Learning AnalyticsLearning AnalyticsLearning analytics is the measurement, collection, analysis and reporting of data about learners and their
Visual analytics
ea ning analytics s e easu e e , co ec o , a a ys s a d epo g o da a abou ea e s a d econtexts, for purposes of understanding and optimizing learning and the environments in which it occurs. (Siemens, 2011)
Visual analytics– It is easier for teachers to understand visualization than statistics
(Breuer et al., 2009)
Data analysis Learning context analysis Learning context analysis
(Cao et al., 2010) Network analysis Network analysis The EC-TEL communities
as an e ample Lehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-3
as an example (Pham et al., 2011)
TeLLNet Learning Analytics Contributions to EC TEL so farto 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 TELSNA as research methodology for TEL
2008 - Petrushyna, Klamma: No Guru, No Method, No Teacher: Self-Observation 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 45.000 Schools: A 2009 Breuer, Klamma, Cao, Vuorikari: Social Network Analysis of 45.000 Schools: A Case Study of Technology Enhanced Learning in Europe
– eTwinning database of European cooperation between schoolsSNA as a tool for teachers– SNA as a tool for teachers
– Visualization and Usability 2010 – Petrushyna: Self-modeling and Self-reflection of E-learning communities
Lehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-4
(Doctoral Consortium)
TeLLNet TeLLNet ProjectTeachers' Lifelong Learning NetworksTeachers' Lifelong Learning Networks
T i i T i S T LLN teTwinning
• Founded in 2005• Coordinated by European S h l t
TwinSpace
• Since 2008?• Subject to eTwinning
W b 2 0 f T i i
TeLLNet
•3-year-project within the EU Lifelong Learning Programme (2009-2012)Schoolnet
• Internet platform with workspace and (communication) tools
P j t t b d b
• Web 2.0 for eTwinning• Blogs• Quality labels• Desktop tools
Programme (2009-2012)•Project obejctives: Competence development for teachers in learning networks with social network • Projects must be done by
two or more partners from different countries• Offline activities: Workshops across Europe
networks with social network analysis and scenario building based on eTwinning• Partners• European SchoolnetWorkshops across Europe European Schoolnet• RWTH Aachen University• Open University of the Netherlands• Institute for Prospective Institute for Prospective Technological Studies (IPTS) –Joint Research Centre of the European Commission
Lehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-5
TeLLNet
Competence and Meta-Competence
Author DefinitionM Cl ll d Th k l d kill i i d
Developed in lots of areas: Human resource management McClelland
(1973)The knowledge, skills, traits, attitudes, self‐concepts, values, or motives directly related to job performance or important life outcomes and shown to differentiate b i d
Human resource management, vocational education ...
Different definitions in literaturesbetween superior and average performers.
Brown and McCartney ( )
A meta‐competence is the overarching ability under which competence shelters.
b h h h d b l
Common points A set of human characteristics
(knowledge skills abilities ) (1995) It embraces the higher order abilities which have to do with being able to learn, adapt, anticipate and create. Meta‐competences are a prerequisite for the d l f h
(knowledge, skills, abilities...) The performances to enhance Categorized into different types
development of capacities such as judgment, intuition and acumen upon which competences are based and without which competences cannot fl i h
Assessment methods Explicit assessment (questionnaire, test) Implicit assessment flourish
Cheethamand Chivers(2005)
Meta‐competence is the competence that is beyond other competences, and which enables individuals to monitor and/or d l h
Implicit assessment Events to monitor Algorithms to design
C t t tLehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-6
develop other competences Competence to computer Automated executable without participation
of questionnaires
TeLLNet
Teachers’ Competence in eTwinning eTwinning Network
Teachers’ Competence in eTwinning Our meta-competenceg
(as of the end of 2010) Teacher Amount %
Sum 135,351 100%
– Higher order competence– Competence to monitor and
develop other competencesProject with projects 26,365 19.4%
with QLs 2,093 1.55%
with EQLs 616 0.46%
develop other competences– Depends on context– Ability to self-monitoring is
with prizes 655 0.48%
Wall post Wall posts sent 10,104 7.47%
Wall posts i d
18,986 14.03%
y gmeta-competence in the contextof LLL Meta
competencereceived
Blog Posts written 4,508 3.33%
Post comments written
441 0.33%
Self- monitoringability
Languaget
Wall-post writing bilitet
ence
e
Post comments received
727 0.54%
Comment Project comments itt
1,531 1.13%
competence
Project performance
ability
Blog writing ability
onal
com
pe
ompe
tenc
e
Lehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-7
written
Prize comments written
354 0.26% Project efficiencyetc.
Comment writing ability, etc.
Prof
essi
o
Soci
alco
TeLLNet Data Analysis: Large-Scale Data Set of eTwinningof eTwinning
New tables generated Table Name Records Error
New data for Web 2.0 – Blogs (TwinBlogPost)
Table Name Records number
Error Rate
Affectation 99886 0.00002Institution 71988 0– Comments (TwinBlogComment,
PrizeComment)– Labels (QualityLabel)
Institution 71988 0MyContact 464780 0.000037Prize 892 0.0045PrizeComment 441 0 0091Labels (QualityLabel)
– Tagging, etc. – ProjectMember
PrizeComment 441 0.0091Project 17392 0ProjectGuestBook 3460 0.009ProjectMember 66145 0 0045
– ProjectGuestBook Data cleaning
ProjectMember 66145 0.0045QualityLabel 4886 0Teacher 133693 0TeacherWall 34900 0 00014
Data dumps– 1st Dump (June, 2010)
2 ( 2010)
TeacherWall 34900 0.00014TwinBlog 15235 0.00013TwinBlogComment 2950 0.2783T i Bl P t 31163 0 00064
Lehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-8
– 2nd Dump (November, 2010)– 3nd Dump (May, 2011)
TwinBlogPost 31163 0.00064Sum 947811 0.0013
TeLLNet
Competence AssessmentCompetence Assessment Indicator model in Entity Relationship DiagramIndicator model in Entity Relationship Diagram
Performance Indicator
Ff
f fNormwI )(
Lehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-9
f
TeLLNet System Architecture ofPrototype CAfePrototype CAfe
Lehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-10
TeLLNet Self-monitoring of Teacher Network in CAfein CAfe
Target users– European teachers (teachers‘ workshops)– Administrators & policy-makers
Lehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-11
TeLLNet Self-Monitoring of Competence ManagementManagement
Lehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-12
TeLLNet Self-Monitoring of Competence Management
Community level ->
Management
Teacher level
Lehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-13
TeLLNetDynamic Network Analysis in Progress
The Development Model (Pham et al. 2011 )
Applied to collaboration (project, email) and social media(blog) networksLehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-14
pp ed o co abo a o (p ojec , e a ) a d soc a ed a(b og) e o s- To detect the development pattern of project partner community- To compare different networks
TeLLNet Learning Analytics: EC-TEL Community among TEL CommunitiesCommunity among TEL Communities ICALT, ICWL, EC-TEL, IST, AIED (Pham, Derntl and Klamma 2011), , , , ( , )
103
104
r of e
dges
(a) Densification law
ICALT: 0.34889*x1.19760.9
0.95
1
g co
effic
ient
(b) Clustering Coefficient
ICALT
101 102 103 104101
102
Num
ber
Number of nodes
ICWL: 1.1149*x1.0544
ECTEL: 0.40338*x 1.2415
ITS: 0.15818*x 1.3817
AIED: 1.0128*x1.1197
1 2 3 4 5 6 7 8 90.75
0.8
0.85
Clu
ster
ing
Age
ICALTICWLECTELITSAIED
0 08 (c) Maximum Betweenness0 7
(d) Largest connected component
0.04
0.06
0.08
mum
bet
wee
nnes
s
ICALTICWLECTELITSAIED
0 2
0.3
0.4
0.5
0.6
0.7
conn
ecte
d co
mpo
nent ICALT
ICWLECTELITSAIED
1 2 3 4 5 6 7 8 90
0.02
Max
im
Age
1 2 3 4 5 6 7 8 9
0
0.1
0.2
Larg
est c
Age
20(e) Diameter
ICALT
8(f) Average Path Length
ICALT
5
10
15
Dia
met
er
ICWLECTELITSAIED
2
4
6
Ave
rage
pat
h le
ngth ICWL
ECTELITSAIED
Lehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-15
1 2 3 4 5 6 7 8 90
Age
1 2 3 4 5 6 7 8 9
0
Age
TeLLNet Node Level Analysis: Structural Holes, Closure and Social CapitalClosure and Social Capital
Structural holes (Burt, 1992)- Nodes are positioned at the interface
between groups (gatekeepers, e.g. node B)Informational nodes: access to information - Informational nodes: access to information from different parts of networks
- Novel ideas by combining information from different groups
- Control the communication between groups
Cl Closure: - Nodes with high clustering coefficient (e.g. node A): embedded in tightly-knit
groupsgroups- More trust and security within coherent communities
Social capital (Coleman, 1990)Lehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-16
p ( , )- Individuals and groups deriving benefits from social relationships- Network structural property: can be either structural hole or closure
TeLLNet
ConclusionsConclusions SNA & visualization as tools for competence development in SNA & visualization as tools for competence development in
learning networks– Competence assessment is still limited in performance indicationCompetence assessment is still limited in performance indication
eTwinning case studyComplex data management issues– Complex data management issues
– Visual complexity of networks vs. teachers’ competence Experimenting with web based tools– Experimenting with web-based tools
Learning analytics is the solution for large scale network
Data analysis
Visual analytics
Contextanalytics
Network analysis
Learning analytics
Lehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-17
analysis analytics analytics analysis analytics
TeLLNet Learning Analytics for Conference ParticipantsParticipants
At academic conferences/workshopsWhi h t lk t tt d?– Which talk to attend?
– To whom to talk to? CAMRS – Mobile Context-aware Recom-
mendation Services for Conference Participants
???
Room 342: workshopAuditorium: keynote
??
Lehrstuhl Informatik 5(Informationssysteme)
Prof. Dr. M. JarkeI5-SPCK-0911-18 Room 204: paper session Hall: poster session Room 048: round table
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