Learning Analytics in a Mobile World - A Community Information Systems Perspective


Published on

Ralf Klamma
RWTH Aachen University
Advanced Community Information Systems (ACIS)

e- Seminar
March 16, 2012
Madrid, Spain

Published in: Education, Business

Learning Analytics in a Mobile World - A Community Information Systems Perspective

  1. 1. TeLLNet Learning Analytics in a Mobile World A Community Information Systems Perspective Ralf Klamma RWTH Aachen University Advanced Community Information Systems (ACIS) klamma@dbis.rwth-aachen.de This work by Ralf Klamma is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported.
  2. 2. ACIS @ RWTH TeLLNetCommunity Information Systems Learning Analytics LA Use Cases Agenda Conclusions & Outlook
  3. 3. Abstract With the increasing availability of smart phones and tablets as well asTeLLNet growing mobile bandwidth, mobile learning offers by the means of apps and electronic books become a commodity. In this presentation I motivate by examples that professional communities need learning support beyond the commodity level. Learning analytics in such settings is more than simple assessment strategies but need a deep understanding of interactions between learners and systems, learner and learning resources as well as learners among each others. Such a perspective is delivered by community information systems serving the needs of mobile communities. The meaningful combination of quantitative and qualitative assessment strategies supports the understanding of learner goals, learning processes and community reflection. Case studies from ongoing EU research projects like ROLE, GALA and TELMAP will support the argumentation.
  4. 4. RWTH Aachen University • 260 institutes in 9 faculties as Europe’sTeLLNet leading institutions for science and research • Currently around 31,400 students are enrolled in over 100 academic programs • Over 5,000 of them are international students hailing from 120 different countries • 1,250 spin-off businesses have created around 30,000 jobs in the greater Aachen region over the past 20 years. • IDEA League • Germany’s Excellence Initiative: 3 clusters of excellence, a graduate school and the institutional strategy “RWTH Aachen 2020: Meeting Global Challenges”
  5. 5. Advanced Community Information Systems (ACIS)TeLLNet Responsive Web Engineering Open Community Web Analytics Visualization Community and Information Simulation Systems Community Community Support Analytics Requirements Engineering
  6. 6. ROLE: Self- and Community Regulated Learning ProcessesTeLLNet The Horizon Report – 2011 Edition Based on Fruhmann, Nussbaumer, Albert, 2010
  7. 7. Communities of PracticeTeLLNet  Community of practice (CoP) as the basic concept for community information systems  Communities of practice are groups of people who share a concern or a passion for something they do and who interact regularly to learn how to do it better (Wenger, 1998)  Usability & sociability (Preece, 2000)
  8. 8. Learning Analytics Support  Interdisciplinary multidimensional model of learning networksTeLLNet – 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) Communities of practice
  9. 9. ROLE Social RE – i* Strategic RationaleTeLLNet
  10. 10. MobSOS: Mobile Service Oracle for SuccessTeLLNet  Context-Aware Usage/Error Statistics  Social Network Analysis  Service Quality Analysis  Visualizations  Set of MobSOS Widgets & Services  interactive data mining  visualizations Dominik Renzel, Ralf Klamma Semantic Monitoring and Analyzing Context-aware Collaborative Multimedia Services 2009 IEEE International Conference on Semantic Computing, 14-16 September 2009 / Berkeley, CA, USA
  11. 11. MediaBase: Cross Media SNA  Collection of Social SoftwareTeLLNet artifacts with parameterized PERL scripts – Blogs & Wikis – Mails & Forums – Web pages  Database support by IBM DB2, eXist, Oracle, ...  Web Interface based on Firefox Plugin, Plone, Drupal, LAS, ... – www.learningfrontiers.eu – www.prolearn-academy.org  Strategies of visualization – Tree maps – Cross-media graphs Klamma et al.: Pattern-Based Cross Media Social Network Analysis for Technology Enhanced Learning in Europe, EC-TEL 2006
  12. 12. Case I: Preparation for English Language Tests  Urch Forums (formerly TestMagic) User of cliqueTeLLNet Non-clique – Community on preparation for English User in thread language tests Clique-user Thread 1 Thread 2 missing in – 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) Time  What are their expressions? – Sentiment Analysis (Phases 3 & 4)  Refinement – 12881 cliques with avg. size 5 and avg. occurrence of 14 Petrushyna, Kravcik, Klamma: Learning Analytics for Communities of Lifelong Learners: a Forum Case. ICALT 2011
  13. 13. Self-Regulated Learning Phases Can Be Observed Different users Phase 1 and 2 (low sentiment, questioner, lot of intents)TeLLNet Phase 3 (increasing sentiment, conversationalist) Phase 4 (high sentiment, answering person) 1 week / step  40% of „footprints“ of cliques align with model for phases
  14. 14. Case II: YouTell - A Web 2.0 Service for Collaborative Storytelling  Collaborative storytelling  TaggingTeLLNet  Web 2.0 Service  Ranking/Feedback  Story search and “pro-  Expert finding sumption”  Recommending Klamma, Cao, Jarke: Storytelling on the Web 2.0 as a New Means of Creating Arts Handbook of Multimedia for Digital Entertainment and Arts, Springer, 2009
  15. 15. Knowledge-Dependent Learning Behaviour in CommunitiesTeLLNet  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  Community tags: experts use more precise tags Renzel, Cao, Lottko, Klamma: Collaborative Video Annotation for Multimedia Sharing between Experts and Amateurs, WISMA 2010, Barcelona, Spain, May 19-20, 2010
  16. 16. Case III: TeLLNet - SNA for European Teachers‘ Life Long Learning  How to manage and handle large scale dataTeLLNet 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? Song, Petrushyna, Cao, Klamma: Learning Analytics at Large: The Lifelong Learning Network of 160, 000 European Teachers. EC-TEL 2011
  17. 17. Analysis and Visualization of Lifelong Learner DataTeLLNet
  18. 18. Advanced Community Information Systems • LAS & • yFiles SNATeLLNet Services • Widgets • youTell 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 • MobSOS • Web Mining • LAS • Recommender • TellNeT • Cloud Systems Computing • Multi Agent • Mobile Simulation Computing Social Requirements Engineering • Agent and Goal Oriented i* Modeling • Participatory Community Design
  19. 19. Conclusions & Outlook  Learning Analytics (LA) in lifelong & mobile learner communities isTeLLNet based on network and data analysis methods  LA framework based on modeling & reflection support – MediaBase: Data Management for LA – MobSOS: Establishment of LA dashboard and widget collections for mobile learning communities  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 – TellNet: Analysis and visualization of large learner networks Performance Indicators and Visual Analytics