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Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
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Supporting Professional Communities in the Next Web

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PWM Wissenstag Social Enterprise @ I-KNOW 2013
Wednesday, September 4, 2013 in Graz (Austria)

Ralf Klamma
Advanced Community Information Systems (ACIS)
RWTH Aachen

Published in: Technology, Business
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  • 1. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 1 Learning Layers This slide deck is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. Supporting Professional Communities in the Next Web Ralf Klamma Advanced Community Information Systems (ACIS) RWTH Aachen University, Germany klamma@dbis.rwth-aachen.de PWM Wissenstag Social Enterprise @ I-KNOW 2013 Wednesday, September 4, 2013 in Graz (Austria)
  • 2. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 2 Learning Layers Responsive Open Community Information Systems Community Visualization and Simulation Community Analytics Community Support WebAnalytics WebEngineering Advanced Community Information Systems (ACIS) Group @ RWTH Aachen Requirements Engineering
  • 3. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 3 Learning Layers Agenda TheNextWeb LearningLayersUse-Case SemanticVideoAnnotation ResponsiveOpenLearning Environment(ROLE) DireWolf-Distributingand MigratingUserInterfacesfor Widget-basedWebApplications Conclusions&Outlook
  • 4. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 4 Learning Layers The Web: Long Tail & Fragments   The Web is a scale-free, fragmented network –  The power law (Pareto-Distribution etc.) –  95 % of users are located in the Long Tail (Communities) –  Trust and passion based cooperation IslandTendrils IN Continent Central Core OUT Continent Tunnels [Barabasi, 2002] [Anderson, 2006]
  • 5. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 5 Learning Layers Communities of Practice   Communities of practice (CoP) 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)   Characterization of Professional Communities –  Shared competence in the domain –  Shared practice over time by interactions –  Expertise based on gaining and having reputation within the CoP –  Being an expert vs. being a layman, a newcomer, an amateur etc. –  Informal leadership –  Identity as an expert depends on the lifecycle of the communities Supporting highly dynamic, locally distributed, multi-disciplinary and heterogeneous communities?
  • 6. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 6 Learning Layers The Next Web will be no Internet of Things … … but a huge collaboration platform for professional communities with (some) of the following features –  P2P Browsers as new Web OS in the Cloud –  Mobile & Social (Location-Based Community Services) –  Near Real-Time (WebRTC, XMPP) –  Multi-Device Operations In the following we will address some of the Web Engineering Challenges
  • 7. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 7 Learning Layers USE CASE – SEMANTIC VIDEO ANNOTATION
  • 8. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 8 Learning Layers Maturing Interacting with People at the workplace Paul discovers a problem at the construction site with PLC equipment ... Generating dynamic Learning Material The regional training center observes the Q&A and links it to their course material ... Q: How to use PLC equipment …? • I have seen this before here … • Last time I did it, I … • Here is something helpful Social Semantic Layer Emerging shared meaning, giving context Energy  Consump.on   Lightning   X3-­‐PVQ  X3-­‐PJC   X3-­‐POZ   PLC  Equipment   Instructional Taxonomy • What is … • How to … • Example of … Tutorial: How to Use PLC What is PLC How to use it? Examples Further Information Hot Questions and Answers Work Practice Taxonomy • Installation • Testing • Operation Peter Paul Mary Interacting in the Physical Workplace Physical workplace is equipped with QR tags, learning materials are delivered just in time ... A list of helpful resources • Tutorials: How to use … • Persons: Peter, Mary, … • Work Practice: Installation,.. • Concepts: PLC, Lightning • Q&A: …, Learning Layers in the Construction Industry
  • 9. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 9 Learning Layers Learning Layers – Scaling up Technologies for Informal Learning in SME Clusters
  • 10. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 10 Learning Layers AnViAnno & SeViAnno: Tools for Semantic Annotations of (Mobile) Multimedia Semantic Mobile Multimedia Services § Collaborative Creation of Semantic Annotations § Advanced Services via Cloud Computing Multimedia semantization § Descriptive Annotations (Search & Locate) § Technical/administrative Annotations § Structural Annotations Mobile Multimedia Acquisition § Capturing and Sharing Meaning § 3D/ real-time/ context-aware
  • 11. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 11 Learning Layers Semantic Video Annotation (SeViAnno)   Aims at capturing and sharing meaning –  In communities of practice –  For personal use   Example –  Markup of text for learning (different colors, etc.)   Different kinds of semantic annotations –  Descriptive (search & locate, e.g. keywords, description) –  Technical/administrative (internal info, e.g. creation, type) –  Structural (storage & presentation, e.g. video segments)
  • 12. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 12 Learning Layers SeViAnno User Interfaces SeViAnno (Web) SeViAnno 2.0 (ROLE Widgets) AnViAnno (Android) DireWolf Sevianno (Distributed User Interface)
  • 13. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 13 Learning Layers RESPONSIVE OPEN LEARNING ENVIRONMENTS
  • 14. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 14 Learning Layers Responsive Open Learning Enviroments (ROLE) 2009-2012 •  Empower the learner to build their own responsive learning environment ROLE Vision •  Awareness and reflection of own learning process Responsiveness •  Individually adapted composition of personal learning environment User-Centered
  • 15. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 15 Learning Layers ROLE Technical Infrastructure   Sucessfully deployed in industry and education   Open Source Software Development Kit   ROLE Widget Store (role-widgetstory.eu)   ROLE Sandbox (role-sandbox.eu)
  • 16. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 16 Learning Layers Space (shared by multiple users) SeViAnno in the ROLE Framework Web application (composed of widgets) Widget (collaborative web component) http://role-sandbox.eu/
  • 17. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 17 Learning Layers ROLE Sandbox – Geospatial & Temporal Access §  Users: 1046 §  Widgets: 523 §  Spaces/Activities: 1377 §  Shared Resources: 3764
  • 18. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 18 Learning Layers DIREWOLF - DISTRIBUTING WEB USER INTERFACES
  • 19. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 19 Learning Layers DireWolf Framework: Distributed User Interfaces (DUI)   Challenge for a web application with only one device   Goal: distribute widgets over multiple devices of the single user when requires full screen video, other widgets become unreachable expand the application to multiple devices Map on iPhone Other widgets on PC Video player on iPad
  • 20. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 20 Learning Layers Motivation Single-deviceUIMulti-devicedistributedUI Smartphones: on-site video capture, geo- tagging on maps and video annotation Laptops and PCs: text editing Tablets: video players with multi-touch interaction Limited space and interaction possibilities Lack of tools & methodologies for Web-based DUIs Trend to using multiple personal devices
  • 21. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 21 Learning Layers Distributing UI in Widget-based Web Applications   Widget – reusable minimal service interfaces   Widget space – container for widget orchestration, collaboration, customization   Widget-based application Typical single-device widget Web application DUI widget Web application with DireWolf
  • 22. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 22 Learning Layers Prototype Web application (composed of widgets) Widget (collaborative web component) EU FP7 ROLE Project http://role-sandbox.eu/Space (shared by multiple users)
  • 23. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 23 Learning Layers Conclusions & Outlook   Learning Layers - Scaling up Technologies for Informal Learning in SME Clusters –  Informal Learning on the Workplace –  Collaborative Semantic Video Annotation   Responsive Open Learning Environments –  ROLE SDK for Near Real-Time Widget-Based Web Applications –  DireWolf – a Framework to facilitate User Interactions on a Federation of Multiple Managed Devices   Mobile Web Apps in Learning Layers

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