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Learning Analytics for the Lifelong Long Tail Learner


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Learning Analytics for the Lifelong Long Tail Learner
Ralf Klamma
RWTH Aachen University
Informatik 5 (DBIS)

CELSTEC, Heerlen, The Netherlands
February 24, 2011

Published in: Technology, Education

Learning Analytics for the Lifelong Long Tail Learner

  1. 1. Informatik 5 (DBIS) RWTH Aachen UniversityTeLLNet GALA Learning Analytics for the Lifelong Long Tail Learner Ralf Klamma RWTH Aachen University CELSTEC, Heerlen, The Netherlands February 24, 2011Lehrstuhl Informatik 5(Informationssysteme) Prof. Dr. M. Jarke I5-KL-111010-1
  2. 2. AgendaTeLLNet GALA Conclusions and Outlook Learning Analytics TELLNET AERCS YouTell ROLELehrstuhl Informatik 5(Informationssysteme) Prof. Dr. M. Jarke I5-KL-111010-2
  3. 3. Self- and Community Regulated Learning ProcessesTeLLNet GALA The Horizon Report – 2011 EditionLehrstuhl Informatik 5(Informationssysteme) Prof. Dr. M. Jarke I5-KL-111010-3 Based on [Fruhmann, Nussbaumer & Albert, 2010]
  4. 4. Learning Communities: The Long Tail & Fragments IN Continent Central Core OUT ContinentTeLLNet 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. 5. Learning Analytics Support  Interdisciplinary multidimensional model of learning networksTeLLNet – 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 membersLehrstuhl Informatik 5 Members (Social Network Analysis: Centrality,(Informationssysteme) Prof. Dr. M. Jarke Efficiency) Communities of practice I5-KL-111010-5
  6. 6. MediaBase  Collection of Social Software artifacts with parameterizedTeLLNet 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 mapsLehrstuhl 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. 7. Case I: Preparation for English Language Tests  Urch Forums (formerly TestMagic) User of clique Non-clique – Community on preparation for English User in threadTeLLNet 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 togetherLehrstuhl Informatik 5 – 12881 cliques with avg. size 5 and(Informationssysteme) Prof. Dr. M. Jarke avg. occurrence of 14 I5-KL-111010-7
  8. 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 / stepLehrstuhl Informatik 5(Informationssysteme) Prof. Dr. M. Jarke  40% of „footprints“ of cliques align with model for phases I5-KL-111010-8
  9. 9. Case II: YouTell - A Web 2.0 Service for Collaborative Storytelling  Collaborative storytelling  Tagging  Web 2.0 Service  Ranking/FeedbackTeLLNet  Story search and “pro-  Expert finding GALA sumption”  RecommendingLehrstuhl Informatik 5(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. 10. Knowledge-Dependent Learning Behaviour in CommunitiesTeLLNet 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 frequentlyLehrstuhl Informatik 5  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. 11. Case III: AERCS - Recommendation of Venues for Young Computer Scientists  DBLP (http://www.informatik.uni- - 788,259 author’s names GALA - 1,226,412 publications - 3,490 venues (conferences, workshops, journals)  CiteSeerX ( - 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 andLehrstuhl 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. 12. Properties of Collaboration and Citation Graphs of VenuesTeLLNet GALALehrstuhl Informatik 5(Informationssysteme) Prof. Dr. M. Jarke I5-KL-111010-12
  13. 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. 14. Analysis and Visualization of Lifelong Learner Data  Performance Data on Projects  Network Structures and PatternsTeLLNet GALALehrstuhl Informatik 5(Informationssysteme) Prof. Dr. M. Jarke I5-KL-111010-14
  15. 15. Conclusions & Outlook  Learning Analytics (LA) in lifelong learner communities is based onTeLLNet 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 forLehrstuhl Informatik 5(Informationssysteme) learning communities Prof. Dr. M. Jarke I5-KL-111010-15