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Community Analytics – An Information Systems Perspective

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Community Analytics – An Information Systems Perspective …

Community Analytics – An Information Systems Perspective
Ralf Klamma
CRIWG 2012
Raesfeld, Germany, September 17, 2012

Published in: Education, Technology, Business

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  • 1. TeLLNet Community Analytics – y y An Information Systems Perspective Ralf Klamma & ACIS Groupp RWTH Aachen University Advanced Community Information Systems (ACIS) klamma@dbis.rwth-aachen.deLehrstuhl Informatik 5 CRIWG 2012 R f ld G 2012, Raesfeld, Germany, S t b 17, 2012 September 17(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-1 This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
  • 2. Advanced Community Information Systems (ACIS)TeLLNet ering Responsive Community We Analytics Open Visualization Community eb andd nginee Information Simulation Systems Web En Community Community Support Analytics WLehrstuhl Informatik 5 Requirements R i t(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-2 Engineering
  • 3. AgendaTeLLNet nformation Systems Conclusions & Outllook tics Commu Analyt Use Cases unity mmunity In ComLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-3
  • 4. Abstract  Information Systems serve the needs of organizations. With the y gTeLLNet widespread availability of free Web-based tools and social networking sites also communities with no institutional backing intensify the use of the W b In this th Web. I thi presentation, I motivate b examples th t t ti ti t by l that professional communities need community support beyond the commodity level Community analytics in such settings need a deep level. understanding of interactions between community members and systems, members and resources as well as members among each y g others. Such a perspective is delivered by community information systems serving the needs of professional communities. The meaningfull combination of quantitative and qualitative analytics i f bi ti f tit ti d lit ti l ti strategies supports the understanding of community goals, community processes and community reflection Case studies from ongoing EU reflection.Lehrstuhl Informatik 5(Information Systems) research projects will support the argumentation. Prof. Dr. M. JarkeI5-Klamma-0912-4
  • 5. TeLLNet COMMUNITY INFORMATION SYSTEMSLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-5
  • 6. A Brief History of Community Information Systems Organisational Memories M iTeLLNet (XML, HTML, Communities of XTM) (Web 2.0) Practice Business Processes Social Software Semantic (XML, HTTP, Web RSS) (XML, RDF, Meta Ontologien) Groupware / Data Workflows E-Learning EL i Media (XML, (XML (XML, LOM, Traces BPEL) XML-RPC) Multimedia Web Services (XML, VRML, (XML, WSDL, Digital Media SOAP,UDDI) DC, MPEG) TechnologyLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke Klamma: Social Software and Community Information Systems, 2010I5-Klamma-0912-6
  • 7. Communities of Practice  Communities of practice ( p (CoP) are groups of p p ) g p peopleTeLLNet who share a concern or a passion for something they do and who interact regularly to learn how to do it g y better (Wenger, 1998)  Community Analytics Support – How can CoPs record their complex complex media traces and how they can deal with them? – Can CoPs continuously elicitate and implement requirements? How much computer science support is needed? – C C P llearn meaningfull di it l sociall I t Can CoPs i f digital i Interaction and make use ti d k of disturbances? – Can CoPs maintain or even improve their agency (Learning (Learning,Lehrstuhl Informatik 5(Information Systems) Researching, Working) in the Web 2.0? Prof. Dr. M. JarkeI5-Klamma-0912-7
  • 8. TeLLNet COMMUNITY ANALYTICSLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-8
  • 9. Proposed Professional Development of the Community Analytics Field  Will happen  Big Data by Digital Eco Systems ( pp g y g y (Quantitative Analysis) y )TeLLNet – A plethora of targets (Small Birds) – Professional Communities are distributed in a long tail – Professional Communities use a digital eco system – An arsenal of weapons (Big Guns) – A growing number of community analytics methods – Combined methods from machine intelligence and knowledge representation  May t happen  D M not h Deep I l Involvment with community t ith it (Qualitative Analysis) – Domain knowledge for sense making – Passion for community and sense of belonging – Community learns as a whole → Community Analytics for the Community by the CommunityLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-9
  • 10. Interdisciplinary Multidimensional Model of Communities  Collection of CoP Digital Traces in a MediaBaseTeLLNet – Post-Mortem Crawlers – Real-time, mobile, protocol-based (MobSOS) – (Automatic) metadata generation by Social Network Analysis  Social Requirements Engineering with i* Framework for defining goals and dependencies in CoP Social Software Media Networks Network of Artifacts Cross-Media Social Network Content Analysis on Microcontent, Blog entry, Message, Analysis on Wiki Blog Podcast Wiki, Blog, Podcast, Burst, Th d C B t Thread, Comment, Conversation, Feedback (Rating) t C ti F db k (R ti ) IM, Chat, Email, Newsgroup, Chat … Web 2.0 Business Processes (i*) (Structural, Cross-media) Network of MembersLehrstuhl Informatik 5 Members(Information Systems) (Social Network Analysis: Centrality, Efficiency, Community Detection) Prof. Dr. M. Jarke Communities of practiceI5-Klamma-0912-10
  • 11. MediaBase: Cross Media / Cross Community SNA  Post-Mortem Collection of Attribute has ActorTeLLNet Social Software artifacts with isA parameterized PERL scripts – Blogs & Wikis – Mails & Forums Medium (Social Software) Artifact Member Community – Web pages  Database support by IBM DB2, eXist, Oracle, ...  Web Interface based on Firefox Plugin, Plone, Drupal, LAS, ... – www.learningfrontiers.eu g – www.prolearn-academy.org  Strategies of visualization – Widget-based chartsLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke – Cross-media graphsI5-Klamma-0912-11 Klamma et al.: Pattern-Based Cross Media Social Network Analysis for Technology Enhanced Learning in Europe, EC-TEL 2006
  • 12. Models of Community Success from an Information Systems PerspectiveTeLLNet  Reference Model: D&M IS Success Model (1992) – Based on >100 Empirical/Conceptual Studies p p – Validated by Independent Studies Updated  MobSOS Model: Integration of Future Web Concepts – Mobility – Real-TimeLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke – Protocol-based (HTTP, XMPP, RESTful)I5-Klamma-0912-12
  • 13. MobSOS Survey Module  Testbed: MobSOS SurveyTeLLNet Service – Survey Management – Survey Participation – XML/Relational DB Schema – Questionnaire XML Schema – Adaptive Templates  Client: MobSOS Surveys – S Survey P ti i ti Participation – Mobile ApplicationLehrstuhl Informatik 5 – W bb d Web-based(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-13
  • 14. MobSOS Success Model OverviewTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-14
  • 15. MobSOS Test beds Analytics & Visualization beds,TeLLNet  Context-Aware Usage/Error Statistics  S i l N t k Analysis Social Network A l i  Service Quality Analysis  Visualization  Set of MobSOS Widgets & Services  interactive data mining  visualizationLehrstuhl Informatik 5 Dominik Renzel, Ralf Klamma(Information Systems) Semantic Monitoring and Analyzing Context-aware Collaborative Multimedia Services Prof. Dr. M. JarkeI5-Klamma-0912-15 2009 IEEE International Conference on Semantic Computing, 14-16 September 2009 / Berkeley, CA, USA
  • 16. Community Analytics in CoP  User-to-Service CommunicationTeLLNet • CoP-aware Usage Statistics • Identification of successful CoP services • Identification of CoP service usage patterns  User to User User-to-User Communication • CoP-aware Social Network Analysis • Identification of influential CoP members • Identification of CoP member interaction/learning patternsLehrstuhl Informatik 5(Information Systems) + Prof. Dr. M. JarkeI5-Klamma-0912-16
  • 17. Supporting Community Practice with the MobSOS Success ModelTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-17
  • 18. Community SRE Processes– i* Strategic RationaleTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-18
  • 19. ROLE Requirements Bazaar – Community-aware R C it Requirements P i iti ti i t PrioritizationTeLLNet Community-dependent C it d d t requirements ranking lists Factors influencing requirements ranking User-controlled weighting of ranking factorsLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-19
  • 20. TeLLNet ROLE & TELMAP CASE STUDIESLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-20
  • 21. Research Context: Responsive Open Learning Environments (ROLE)TeLLNet Focus of key research objectives: • Empower the learner to build their ROLE Vi i Vision own responsive learning environment • Awareness and reflection of own Responsiveness l i learning process • Individually adapted composition of User-Centered personal learning environmentLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-21
  • 22. Self-Regulated Self Regulated LearningTeLLNet learner input regarding goals, preferences, … learner profile information is defined and revised evaluation and creating PLE self-evaluation plan learner reflects and reacts learner finds and selects on strategies, achievements, g , , learning resources and usefulness reflect learn recommendations feedback from peers or tutors (from different sources) learner works on selected learning resources l i assessment and attaining skills using different self-assessment learning events (8LEM) recommen-dations be aware of monitoring it i learner should understand and ROLE infrastructure should control own learning process provide adaptive guidanceLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-22
  • 23. 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 pos s, 0,000 eads, 800,000+ posts, thread th d 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 14Lehrstuhl Informatik 5 Petrushyna, Kravcik, Klamma:(Information Systems) Learning Analytics for Communities of Lifelong Learners: a Forum Case. Prof. Dr. M. JarkeI5-Klamma-0912-26 ICALT 2011
  • 24. Self-Regulated Learning Phases Can Be Observed in Communities 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 / stepLehrstuhl Informatik 5(Information Systems)  40% of „footprints“ of cliques align with model for phases Prof. Dr. M. JarkeI5-Klamma-0912-27
  • 25. Research Context: Roadmapping Technology Enhanced LearningTeLLNet Mapping and roadmapping for TEL Understanding the current TEL landscape g p Strong and weak signals for change at different levels Different data sources Different methods, e.g. Delphi, Community modeling, Text analysis Social Network Analysis, etc. analysis, Analysis etcLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-28
  • 26. TEL ProjectsTeLLNet  Project as a funded collaborative R&D effort  Important role in the R&D value chain Points of interest: – Organizational collaboration – Progression of consortia – Impact on the landscapeLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-29
  • 27. Data Set Progr. Call # Projects (acronyms) Call 2005 4 CITER, JEM, MACE, MELTTeLLNet Call 2006 7 COSMOS, EdReNe, EUROGENE, eVip, Intergeo, KeyToNature, Organic.Edunet ECP Call 2007 3 ASPECT, iCOPER, EduTubePlus Call 2008 5 LiLa, Math-Bridge, mEducator, OpenScienceResources, OpenScout IST-2002- CONNECT, E-LEGI, ICLASS, KALEIDOSCOPE, LEACTIVEMATH, PROLEARN, 8 2.3.1.12a TELCERT, UNFOLD , IST-2004- APOSDLE, ARGUNAUT, ATGENTIVE, COOPER, ECIRCUS, ELEKTRA, I-MAESTRO, FP6 2.4.10b 14 KP-LAB, L2C, LEAD, PALETTE, PROLIX, RE.MATH, TENCOMPETENCE IST-2004- ARISE, CALIBRATE ELU EMAPPS COM ICAMP LOGOS LT4EL MGBL UNITE ARISE CALIBRATE, ELU, EMAPPS.COM, ICAMP, LOGOS, LT4EL, MGBL, UNITE, 10 2.4.13c VEMUS ICT-2007.4.1d 6 80DAYS, GRAPPLE, IDSPACE, LTFLL, MATURE, SCY ICT-2007.4.3d 7 COSPATIAL, DYNALEARN, INTELLEO, ROLE, STELLAR, TARGET, XDELIA FP7 ALICE, ARISTOTELE, ECUTE, GALA, IMREAL, ITEC, METAFORA, MIROR, ICT-2009.4.2b 13 MIRROR, NEXT-TELL, SIREN, TEL-MAP, TERENCELehrstuhl Informatik 5 Total: 77(Information Systems) a … Technology-enhanced learning and access to cultural heritage” c … Strengthening the Integration of the ICT research effort in an Enlarged Europe” Prof. Dr. M. JarkeI5-Klamma-0912-30 b … Technology-Enhanced Learning d … Digital libraries and technology-enhanced learning”
  • 28. TEL Projects as Social Networks  Projects x Organizations j gTeLLNet  Project consortium progression – Nodes: Projects IMC, RWTH, ROLE OU, OU ZSI – Ed Edges: O l of consortia Overlap f ti (directed, weighted) TEL-Map  Organizational collaboration – N d O Nodes: Organiziations i i i The Open STELLAR, EUROGENE, University ROLE, PROLEARN, – Edges: Collaboration in iCOPER, ASPECT multiple projects lti l j t KULehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke (undirected, weighted) LeuvenI5-Klamma-0912-31
  • 29. Consortium Progression Network At least 2 overlapping partnersTeLLNet At least 3 months time between project start d t l t th ti b t j t t t dates 68 projects, 198 connections Node size proportional to weighted degree p p g gLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-32
  • 30. Project Impact on the LandscapeTeLLNet  Successor projects relative to opportunity Cumulative fraction of successorLehrstuhl Informatik 5 projects filled up with ps members(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-33 Derntl, Klamma: European TEL Projects Community. EC-TEL 2012.
  • 31. Impact Graph All programmes p g t = 3 monthsTeLLNet k=2 represented, with FP6 strongest Node size proportional to impact Best impact for money: PROLEARN, ICOPER, GRAPPLE All past networks of excellence among top five ranks. pLehrstuhl Informatik 5 Several running or recently The two inaugural(Information Systems) Prof. Dr. M. Jarke completed projects NoEs on topI5-Klamma-0912-34
  • 32. Expected Impact?  Correlation between weighted in-degree and impactTeLLNet iin progression graph i h  Stronger incoming connections appears to lead to higher impact 50 45 ICOPER STELLAR 40 Weighted In-Degree 35 30 GRAPPLE 25 20 ASPECT 15 LTFLL 10 MACE 5 Filter: Project start > 2005 0Lehrstuhl Informatik 5(Information Systems) 0 0.1 0.2 0.3 0.4 Prof. Dr. M. Jarke ImpactI5-Klamma-0912-35
  • 33. Future GazingTeLLNet GALA 53 50 OpenScout 45 ICOPER 46 STELLAR 40 ROLE Weighted In-Degree e 35 32 30 29 GRAPPLE 25 iTEC TEL-MAP TEL MAP d 20 20 ASPECT 15 LTFLL 10 MACE 5 0Lehrstuhl Informatik 5 0 0.1 0.2 0.3 0.4(Information Systems) Prof. Dr. M. Jarke ImpactI5-Klamma-0912-36
  • 34. In-Degree In Degree – “Expected Impact”TeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-37
  • 35. Most Frequent CollaboratorsTeLLNet 1. PROLEARN (FP6): 16 pairs 4. GRAPPLE (FP7): 8 pairs, 2. 2 ICOPER (ECP): 10 pairs 5. 5 STELLAR (FP7) ROLE (FP7) (FP7), (FP7),Lehrstuhl Informatik 5(Information Systems) 3. OpenScout (ECP): 9 pairs PROLIX (FP6): 5 pairs Prof. Dr. M. JarkeI5-Klamma-0912-38
  • 36. Projects Space @ LearningFrontiers.eu LearningFrontiers euTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. JarkeI5-Klamma-0912-39
  • 37. TEL Mediabase Dashboard http://learningfrontiers.eu/?q=dashboardTeLLNet Derntl, Erdtmann, Klamma: AnLehrstuhl Informatik 5(Information Systems) embeddable widget-based dashboard Prof. Dr. M. Jarke for visual analytics on scientificI5-Klamma-0912-40 communities. I-KNOW 2012
  • 38. Advanced Community Information Systems • LAS & Services • SNATeLLNet • ROLE Sandbox • Widgets Responsive • Network • Advanced Community y Models Open Web & Visualization Community • Network Multimedia & Simulation Environments Analysis ring Technologies • Actor Network Web Analytics • XMPP Theory Web Engineer • HTML5 • Communities of • MPEG-7 Community Community Practice • Web Support Analytics • Game Theory Services • Community Detection A • RESTf l RESTful • Requirements • MediaBase Bazaar • MobSOS • Web Mining • LAS • Recommender • Cloud Systems Computing • Multi Agent • Mobile Simulation Sim lation Computing Social Requirements Engineering • Agent and Goal Oriented i* ModelingLehrstuhl Informatik 5(Information Systems) • Participatory Community Design Prof. Dr. M. JarkeI5-Klamma-0912-41