Learning Analytics for the Lifelong Long Tail Learner Ralf Klamma RWTH Aachen University Informatik 5 (DBIS) CELSTEC, Heerlen, The Netherlands February 24, 2011

1. Informatik 5 (DBIS)
RWTH Aachen University
TeLLNet
GALA
Learning Analytics
for the Lifelong Long Tail Learner
Ralf Klamma
RWTH Aachen University
CELSTEC, Heerlen, The Netherlands
February 24, 2011
Lehrstuhl Informatik 5
(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-1

2. Agenda
TeLLNet
GALA
Conclusions and Outlook
Learning Analytics
TELLNET
AERCS
YouTell
ROLE
Lehrstuhl Informatik 5
(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-2

3. Self- and Community Regulated
Learning Processes
TeLLNet
GALA
The Horizon Report – 2011 Edition
Lehrstuhl Informatik 5
(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-3 Based on [Fruhmann, Nussbaumer & Albert, 2010]

4. Learning Communities:
The Long Tail & Fragments
IN Continent Central Core OUT Continent
TeLLNet
GALA
Tunnels
[Anderson, 2006]
Tendrils Island
[Barabasi, 2002]
 The Web is a scale-free, fragmented network
– The power law (Pareto-Distribution etc.)
Lehrstuhl Informatik 5
– 95 % of users are located in the Long Tail (Communities)
(Informationssysteme)
Prof. Dr. M. Jarke – Trust and passion based cooperation
I5-KL-111010-4

5. Learning Analytics Support
 Interdisciplinary multidimensional model of learning networks
TeLLNet
– Social network analysis (SNA) is defining measures for
social relations
GALA
– Actor network theory (ANT) is connecting human and media agents
– i* framework is defining strategic goals and dependencies
– Theory of media transcriptions is studying cross-media knowledge
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
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Members
(Social Network Analysis: Centrality,
(Informationssysteme)
Prof. Dr. M. Jarke
Efficiency)
Communities of practice
I5-KL-111010-5

6. MediaBase
 Collection of Social Software
artifacts with parameterized
TeLLNet
PERL scripts
GALA
– Mailing lists
– Newsletter
– Web sites
– RSS Feeds
– Blogs
 Database support by IBM DB2,
eXist, Oracle, ...
 Web Interface based on Firefox
Plugin, Plone/Zope, Widgets, ...
 Strategies of visualization
– Tree maps
Lehrstuhl Informatik 5
(Informationssysteme)
Prof. Dr. M. Jarke
– Cross-media graphs
I5-KL-111010-6 Klamma et al.: Pattern-Based Cross Media Social Network Analysis for Technology Enhanced Learning in Europe, EC-TEL 2006

7. Case I: Preparation for
English Language Tests
 Urch Forums (formerly TestMagic) User of clique
Non-clique
– Community on preparation for English User in thread
TeLLNet language tests Clique-user
Thread 1 Thread 2 missing in
GALA – 120,000+ threads, 800,000+ posts,
thread
100,000+ users over 10 years
– Social Network Analysis, Machine Thread 3
Learning and Natural Language
Processing
 What are the goals of learners?
– Intent Analysis (Phases 1 & 2)
 What are their expressions?
– Sentiment Analysis (Phases 3 & 4) Time
 Refinement
– Cliques are users who appear in
several threads together
Lehrstuhl Informatik 5 – 12881 cliques with avg. size 5 and
(Informationssysteme)
Prof. Dr. M. Jarke avg. occurrence of 14
I5-KL-111010-7

8. Learning Phases Can Be Observed
Different users Phase 1 and 2 (low sentiment, questioner, lot of intents)
Phase 3 (increasing sentiment, conversationalist)
TeLLNet
Phase 4 (high sentiment, answering person)
GALA
1 week / step
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(Informationssysteme)
Prof. Dr. M. Jarke
 40% of „footprints“ of cliques align with model for phases
I5-KL-111010-8

9. Case II: YouTell - A Web 2.0 Service
for Collaborative Storytelling
 Collaborative storytelling  Tagging
 Web 2.0 Service  Ranking/Feedback
TeLLNet  Story search and “pro-  Expert finding
GALA sumption”  Recommending
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(Informationssysteme)
Prof. Dr. M. Jarke
Klamma, Cao, Jarke: Storytelling on the Web 2.0 as a New Means of Creating Arts
I5-KL-111010-9 Handbook of Multimedia for Digital Entertainment and Arts, Springer, 2009

10. Knowledge-Dependent
Learning Behaviour in Communities
TeLLNet
GALA
 Expert finding algorithm: Knowledge value of community sorted by keywords
 Community behaviors: experts spent more time on the services
 Experts prefers semantic tags while amateurs uses “simple” tags frequently
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 Community tags: experts use more precise tags
(Informationssysteme) Renzel, Cao, Lottko, Klamma: Collaborative Video Annotation for Multimedia Sharing between Experts and Amateurs,
Prof. Dr. M. Jarke
I5-KL-111010-10 WISMA 2010, Barcelona, Spain, May 19-20, 2010

11. Case III: AERCS - Recommendation of
Venues for Young Computer Scientists
 DBLP (http://www.informatik.uni-
trier.de/~ley/db/)
TeLLNet
- 788,259 author’s names
GALA - 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
Lehrstuhl Informatik 5
are cited 2037 times
(Informationssysteme)
Prof. Dr. M. Jarke Pham, Klamma, Jarke: Development of Computer Science Disciplines – A Social Network
I5-KL-111010-11 Analysis Approach, submitted to SNAM, 2011

12. Properties of Collaboration and
Citation Graphs of Venues
TeLLNet
GALA
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Prof. Dr. M. Jarke
I5-KL-111010-12

13. Case IV: TeLLNet - SNA for European
Teachers‘ Life Long Learning
 How to manage and handle large scale
data on social networks?
TeLLNet
 How to analyse social network data in
GALA order to develop teachers’
competence, e.g. to facilitate a better
project collaboration?
 How to make the network visualization
useful for teachers’ lifelong learning?
Lehrstuhl Informatik 5
(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-13

14. Analysis and Visualization of
Lifelong Learner Data
 Performance Data on Projects  Network Structures and Patterns
TeLLNet
GALA
Lehrstuhl Informatik 5
(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-14

15. Conclusions & Outlook
 Learning Analytics (LA) in lifelong learner communities is based on
TeLLNet
network and data analysis methods
GALA  LA framework based on modeling & reflection support
 Four case studies
– ROLE: Goal and sentiment mining for self-regulated learners
Identification of Learning Phases
– YouTell: Expert vs. amateurs in collaborative storytelling communities
Expert Finding Services
– AERCS: Recommendation services based on network analysis
Recommendation Services
– TellNet: Analysis and visualization of large learner networks
Performance Indicators and Visual Analytics
 Establishment of LA dashboard and widget collections for
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learning communities
Prof. Dr. M. Jarke
I5-KL-111010-15