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Community Learning Analytics – A New Research Field in TEL

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Ralf Klamma
Ralf Klamma

Keynote at the 10th JTEL Summer School 2014 in Malta Ralf Klamma ACIS Group @ RWTH Aachen University April 28, 2014

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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. Community Learning Analytics – A New Research Field in TEL Ralf Klamma Advanced Community Information Systems (ACIS) RWTH Aachen University, Germany klamma@dbis.rwth-aachen.de JTEL Summer School, Malta, April 28, 2014
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 2 Learning Layers Abstract Learning Analytics has become a major research area recently. In particular learning institutions seek ways to collect, manage, analyze and exploit data from learners and instructors for the facilitation of formal learning processes. However, in the world of informal learning at the workplace, knowledge gained from formal learning analytics is only applicable on a commodity level. Since professional communities need learning support beyond this level, we need a deep understanding of interactions between learners and other entities in community- regulated learning processes - a conceptual extension of self- regulated learning processes. In this presentation, we discuss scaling challenges for community learning analytics and give both conceptual and technical solutions. We report experiences from ongoing research in this area, in particular from the two EU integrating project ROLE (Responsive Open Learning Environments) and Learning Layers (Scaling up Technologies for Informal Learning in SME Clusters).
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 3 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
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 4 Learning Layers Agenda LearningAnalytics CommunityLearningAnalytics ROLE&LearningLayers ExpertsinCommunityInformation Systems Conclusions&Outlook
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 5 Learning Layers A PHD STUDENT VIEW ON THE RESEARCH FIELD
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 6 Learning Layers Motivations for Doing PhD Research in TEL ■  Some reasons (more?) –  My supervisor told me … (research interest of person paying me) –  My own research interest –  Good career perspectives (get famous, get rich, or both) ■  Formal Learning –  Close to my own practice and experience as a teacher, researcher –  Research settings easier to control (classroom as a lab) ■  Informal Learning –  Better funding opportunities (H2020, industry) –  More innovative (mobile, Web, micro, games) –  Real impact expected
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 7 Learning Layers LEARNING ANALYTICS
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 8 Learning Layers Self- and Community Regulated Learning Processes Based on [Fruhmann, Nussbaumer & Albert, 2010] Learner profile information is defined or revised Learner finds and selects learning resources Learner works on selected learning resources Learner reflects and reacts on strategies, achievements and usefulness plan learnreflect The Horizon Report – 2011 Edition
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 9 Learning Layers The Long Tail of Personal Knowledge in Lifelong Learning ■  Zillions of new learning opportunities ■  Abundance of learning materials ■  But: Extremely challenging to find & navigate High-quality, specially designed, learning materials like books or course material Gaps in personal knowledge identified mostly by real-world practice
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 10 Learning Layers Personal Learning Environment (PLE) PLE describes the tools, communities, and services that constitute the individual educational platforms learners use to direct their own learning and pursue educational goals LMS – course-centric vs. PLE – learner-centric: • Extension of individual research • Students in charge of their learning process • self-direction, responsibility • Promotes authentic learning (incorporating expert feedback) • Student’s scholarly work + own critical reflection + the work and voice of others • Web 2.0 influence on educational process • customizable portals/dashboards, iGoogle, My Yahoo! • Learning is a collaborative exercise in collection, orchestration, remixing, & integration of data into knowledge building • Emphasis on metacognition in learning
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 11 Learning Layers ROLE Approach to the Design of Learning Experiences guidance & freedom of learner motivation of learner (intrinsic, extrinsic) stimulation of learner’s meta- cognition collaboration & good practice sharing among peers personalization & adaptability to learner & context What is the impact of these findings from behavioral & cognitive psychology on design of learning? Goal setting Planning Reflection Control & Responsibility Recommendation
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 12 Learning Layers ROLE Approach to the Design of Learning Experiences What is the impact of these findings from behavioral & cognitive psychology on design of Personal Learning Environments? learner profile information is defined and revised learner finds and selects learning resources learner works on selected learning resources plan learnreflect learner input regarding goals, preferences, … creating PLE recommendations from peers or tutors assessment and self-assessment evaluation and self-evaluation feedback (from different sources) learner should understand and control own learning process ROLE infrastructure should provide adaptive guidance attaining skills using different learning events (8LEM) learner reflects and reacts on strategies, achievements, and usefulness monitoring recommen-dations be aware of
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 13 Learning Layers Learning Analytics vs. Community Learning Analytics Formal Learning Learning Analytics Community Regulated Learning Community Learning Analytics Environment LMS EDM/VA CIS/ROLE DM/VA/SNA/Role Mining Tools Fixed LMS Specific Eco-System Tool Recommender Activities Fixed Content Recommender Dynamic Content Recommender / Expert Recommender Goals Fixed Progress Dynamic Progess / Goal Mining / Refinement Communities Fixed Not applicable Dynamic (Overlapping) Community Detection Use Cases Courses Learning Paths Peer Production / Scaffolding Semantic Networks of Learners / Annotations
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 14 Learning Layers COMMUNITY LEARNING ANALYTICS – A GENERAL APPROACH
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 15 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 experts in CoP –  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 Expertise in highly dynamic, locally distributed multi-disciplinary and heterogeneous communities?
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 16 Learning Layers Proposed Development of the Community Learning Analytics Field ■  Will happen J Big Data by Digital Eco Systems (Quantitative Analysis) –  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 learning analytics methods –  Combined methods from machine intelligence and knowledge representation ■  May not happen L Deep Involvment with community (Qualitative Analysis) –  Domain knowledge for sense making –  Passion for community and sense of belonging –  Community learns as a whole → Community Learning Analytics for the Community by the Community
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 17 Learning Layers Web 2.0 Competence Development Cultural and Technological Shift by Social Software Impact on Knowledge Work Impact on Professional Communities Web 1.0 Web 2.0 Microcontent Providing commentary Personal knowledge publishing Establishing personal networks Testing Ideas Social learning Identifying competences Emergent Collaboration Trust & Social capital personal website and content management blogging and wikis User generated content Participation directories (taxonomy) and stickiness Tagging ("folksonomy") and syndication Ranking Sense-making Remixing Aggregation Embedding Emergent Metadata Collective intelligence Wisdom of the Crowd Collaborative Filtering Visualizing Knowledge Networks
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 18 Learning Layers Interdisciplinary Multidimensional Model of Communities ■  Collection of CoP Digital Traces in a MediaBase –  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 Cross-Media Social Network Analysis on Wiki, Blog, Podcast, IM, Chat, Email, Newsgroup, Chat … Web 2.0 Business Processes (i*) (Structural, Cross-media) Members (Social Network Analysis: Centrality, Efficiency, Community Detection) Network of Artifacts Content Analysis on Microcontent, Blog entry, Message, Burst, Thread, Comment, Conversation, Feedback (Rating) Network of Members Communities of practice Media Networks
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 19 Learning Layers Community Learning Analytics in CoP ■  User-to-Service Communication •  CoP-aware Usage Statistics •  Identification of successful CoP services •  Identification of CoP service usage patterns ■  User-to-User Communication •  CoP-aware Social Network Analysis •  Identification of influential CoP members •  Identification of CoP member interaction/learning patterns +
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 20 Learning Layers Supporting Community Practice with the MobSOS Success Model
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 21 Learning Layers Community SRE Processes– i* Strategic Rationale
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 22 Learning Layers RESPONSIVE OPEN LEARNING ENVIRONMENTS
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 23 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
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 24 Learning Layers ROLE Technical Infrastructure ■  Sucessfully deployed in industry and education ■  Open Source Software Development Kit ■  ROLE Widget Store (role-widgetstore.eu) ■  ROLE Sandbox (role-sandbox.eu)
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 25 Learning Layers ROLE Sandbox – Geospatial & Temporal Access §  Users: 5787 (95% external) §  Widgets: 1475 (71.5% external) §  Spaces: 1283 (64.3% external) §  Shared Resources: 18922 (6% external)
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 26 Learning Layers ROLE Requirements Bazaar – Community-aware Requirements Prioritization Factors influencing requirements ranking User-controlled weighting of ranking factors Community-dependent requirements ranking lists http://requirements-bazaar.org
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 27 Learning Layers Learning Analytics Visualization – Dashboards 1.  Database Selection 2.  Filter Selection/ Definition 3.  Adapted Visualization
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 28 Learning Layers LEARNING LAYERS – SCALING UP TECHNOLOGIES FOR INFORMAL LEARNING IN SME CLUSTERS
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 29 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
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 30 Learning Layers Learning Layers – Scaling Technologies for Informal Learning Learning Layers – Scaling up Technologies for Informal Learning in SME Clusters
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 31 Learning Layers Space (shared by multiple users) Using the ROLE Framework for Semantic Video Annotation Web application (composed of widgets) Widget (collaborative web component) http://role-sandbox.eu/
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 32 Learning Layers SeViAnno Prototypes SeViAnno (Web) SeViAnno 2.0 (Widgets) AnViAnno (Android) AchSo! (Android)
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 33 Learning Layers COMMUNITY LEARNING ANALYTICS – EXPERT IDENTIFICATION
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 34 Learning Layers Experts in Learning Communities ■  In learning communities many experts from different fields meet –  Intergenerational learning –  Interdisciplinary learning ■  New Openness for Amateur Contributions ■  Methods, Tools & CoP co-develop –  Expert role models needed –  Expert identification based on complex media traces
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 35 Learning Layers YouTell - A Web 2.0 Service for Collaborative Storytelling §  Collaborative storytelling §  Web 2.0 Service §  Story search and “pro-sumption” §  Tagging §  Ranking/Feedback §  Expert finding §  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
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 36 Learning Layers Expert Finding – Computation of Actual Knowledge ■  Data vector consists of –  Personal data vector –  Competences, skills, qualification profile –  Self-entered data –  Story data vector –  Visits of stories –  Involvement in projects –  Expert data vector –  Advice given –  Advice received –  Value = #Keywords – Date Decay – Feedback Motivation PESE: Web 2.0 –Anwen- dung für community- basiertes Storytelling Der PESE- Prototyp Evaluierung des Prototypen Zusammen- fassung Ausblick Find the most appropriate expert Data vector represents knowledge of the expert
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 37 Learning Layers Knowledge-Dependent Learning Behaviour in Communities Renzel, Cao, Lottko, Klamma: Collaborative Video Annotation for Multimedia Sharing between Experts and Amateurs, WISMA 2010, Barcelona, Spain, May 19-20, 2010 §  Expert finding algorithm: Knowledge value of community sorted by keywords §  Community behavior: Experts spent more time on the services §  Experts prefers semantic tags while amateurs uses “simple” tags frequently §  Community tags: Experts use more precise tags
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 38 Learning Layers Threads to Expert Finding ■  Compromising techniques —  Sybil attack [Douc 2002], Reputation theft, Whitewashing attack, etc.. —  Compromising the input and the output of the expert identification algorithm ■  Example: Sybil attacks —  Fundamental problem in open collaborative Web systems —  A malicious user creates many fake accounts (Sybils) which all reference the user to boost his reputation (attacker’s goal is to be higher up in the rankings) Sybil  region  Honest  region   ABack  edges  
Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 39 Learning Layers Conclusions & Outlook ■  Community Learning Analytics –  Informal learning more challenging for learning analytics –  New research challenges and funding opportunities –  Highly interdisciplinary and multi-method research ■  Case Studies –  Responsive Open Learning Environments – ROLE SDK for Near Real-Time Widget-Based Web Applications –  Learning Layers - Scaling up Technologies for Informal Learning in SME Clusters – Informal Learning on the Workplace – Collaborative Semantic Video Annotation

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Community Learning Analytics – A New Research Field in TEL

  • 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. Community Learning Analytics – A New Research Field in TEL Ralf Klamma Advanced Community Information Systems (ACIS) RWTH Aachen University, Germany klamma@dbis.rwth-aachen.de JTEL Summer School, Malta, April 28, 2014
  • 2. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 2 Learning Layers Abstract Learning Analytics has become a major research area recently. In particular learning institutions seek ways to collect, manage, analyze and exploit data from learners and instructors for the facilitation of formal learning processes. However, in the world of informal learning at the workplace, knowledge gained from formal learning analytics is only applicable on a commodity level. Since professional communities need learning support beyond this level, we need a deep understanding of interactions between learners and other entities in community- regulated learning processes - a conceptual extension of self- regulated learning processes. In this presentation, we discuss scaling challenges for community learning analytics and give both conceptual and technical solutions. We report experiences from ongoing research in this area, in particular from the two EU integrating project ROLE (Responsive Open Learning Environments) and Learning Layers (Scaling up Technologies for Informal Learning in SME Clusters).
  • 3. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 3 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
  • 4. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 4 Learning Layers Agenda LearningAnalytics CommunityLearningAnalytics ROLE&LearningLayers ExpertsinCommunityInformation Systems Conclusions&Outlook
  • 5. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 5 Learning Layers A PHD STUDENT VIEW ON THE RESEARCH FIELD
  • 6. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 6 Learning Layers Motivations for Doing PhD Research in TEL ■  Some reasons (more?) –  My supervisor told me … (research interest of person paying me) –  My own research interest –  Good career perspectives (get famous, get rich, or both) ■  Formal Learning –  Close to my own practice and experience as a teacher, researcher –  Research settings easier to control (classroom as a lab) ■  Informal Learning –  Better funding opportunities (H2020, industry) –  More innovative (mobile, Web, micro, games) –  Real impact expected
  • 7. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 7 Learning Layers LEARNING ANALYTICS
  • 8. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 8 Learning Layers Self- and Community Regulated Learning Processes Based on [Fruhmann, Nussbaumer & Albert, 2010] Learner profile information is defined or revised Learner finds and selects learning resources Learner works on selected learning resources Learner reflects and reacts on strategies, achievements and usefulness plan learnreflect The Horizon Report – 2011 Edition
  • 9. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 9 Learning Layers The Long Tail of Personal Knowledge in Lifelong Learning ■  Zillions of new learning opportunities ■  Abundance of learning materials ■  But: Extremely challenging to find & navigate High-quality, specially designed, learning materials like books or course material Gaps in personal knowledge identified mostly by real-world practice
  • 10. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 10 Learning Layers Personal Learning Environment (PLE) PLE describes the tools, communities, and services that constitute the individual educational platforms learners use to direct their own learning and pursue educational goals LMS – course-centric vs. PLE – learner-centric: • Extension of individual research • Students in charge of their learning process • self-direction, responsibility • Promotes authentic learning (incorporating expert feedback) • Student’s scholarly work + own critical reflection + the work and voice of others • Web 2.0 influence on educational process • customizable portals/dashboards, iGoogle, My Yahoo! • Learning is a collaborative exercise in collection, orchestration, remixing, & integration of data into knowledge building • Emphasis on metacognition in learning
  • 11. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 11 Learning Layers ROLE Approach to the Design of Learning Experiences guidance & freedom of learner motivation of learner (intrinsic, extrinsic) stimulation of learner’s meta- cognition collaboration & good practice sharing among peers personalization & adaptability to learner & context What is the impact of these findings from behavioral & cognitive psychology on design of learning? Goal setting Planning Reflection Control & Responsibility Recommendation
  • 12. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 12 Learning Layers ROLE Approach to the Design of Learning Experiences What is the impact of these findings from behavioral & cognitive psychology on design of Personal Learning Environments? learner profile information is defined and revised learner finds and selects learning resources learner works on selected learning resources plan learnreflect learner input regarding goals, preferences, … creating PLE recommendations from peers or tutors assessment and self-assessment evaluation and self-evaluation feedback (from different sources) learner should understand and control own learning process ROLE infrastructure should provide adaptive guidance attaining skills using different learning events (8LEM) learner reflects and reacts on strategies, achievements, and usefulness monitoring recommen-dations be aware of
  • 13. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 13 Learning Layers Learning Analytics vs. Community Learning Analytics Formal Learning Learning Analytics Community Regulated Learning Community Learning Analytics Environment LMS EDM/VA CIS/ROLE DM/VA/SNA/Role Mining Tools Fixed LMS Specific Eco-System Tool Recommender Activities Fixed Content Recommender Dynamic Content Recommender / Expert Recommender Goals Fixed Progress Dynamic Progess / Goal Mining / Refinement Communities Fixed Not applicable Dynamic (Overlapping) Community Detection Use Cases Courses Learning Paths Peer Production / Scaffolding Semantic Networks of Learners / Annotations
  • 14. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 14 Learning Layers COMMUNITY LEARNING ANALYTICS – A GENERAL APPROACH
  • 15. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 15 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 experts in CoP –  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 Expertise in highly dynamic, locally distributed multi-disciplinary and heterogeneous communities?
  • 16. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 16 Learning Layers Proposed Development of the Community Learning Analytics Field ■  Will happen J Big Data by Digital Eco Systems (Quantitative Analysis) –  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 learning analytics methods –  Combined methods from machine intelligence and knowledge representation ■  May not happen L Deep Involvment with community (Qualitative Analysis) –  Domain knowledge for sense making –  Passion for community and sense of belonging –  Community learns as a whole → Community Learning Analytics for the Community by the Community
  • 17. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 17 Learning Layers Web 2.0 Competence Development Cultural and Technological Shift by Social Software Impact on Knowledge Work Impact on Professional Communities Web 1.0 Web 2.0 Microcontent Providing commentary Personal knowledge publishing Establishing personal networks Testing Ideas Social learning Identifying competences Emergent Collaboration Trust & Social capital personal website and content management blogging and wikis User generated content Participation directories (taxonomy) and stickiness Tagging ("folksonomy") and syndication Ranking Sense-making Remixing Aggregation Embedding Emergent Metadata Collective intelligence Wisdom of the Crowd Collaborative Filtering Visualizing Knowledge Networks
  • 18. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 18 Learning Layers Interdisciplinary Multidimensional Model of Communities ■  Collection of CoP Digital Traces in a MediaBase –  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 Cross-Media Social Network Analysis on Wiki, Blog, Podcast, IM, Chat, Email, Newsgroup, Chat … Web 2.0 Business Processes (i*) (Structural, Cross-media) Members (Social Network Analysis: Centrality, Efficiency, Community Detection) Network of Artifacts Content Analysis on Microcontent, Blog entry, Message, Burst, Thread, Comment, Conversation, Feedback (Rating) Network of Members Communities of practice Media Networks
  • 19. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 19 Learning Layers Community Learning Analytics in CoP ■  User-to-Service Communication •  CoP-aware Usage Statistics •  Identification of successful CoP services •  Identification of CoP service usage patterns ■  User-to-User Communication •  CoP-aware Social Network Analysis •  Identification of influential CoP members •  Identification of CoP member interaction/learning patterns +
  • 20. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 20 Learning Layers Supporting Community Practice with the MobSOS Success Model
  • 21. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 21 Learning Layers Community SRE Processes– i* Strategic Rationale
  • 22. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 22 Learning Layers RESPONSIVE OPEN LEARNING ENVIRONMENTS
  • 23. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 23 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
  • 24. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 24 Learning Layers ROLE Technical Infrastructure ■  Sucessfully deployed in industry and education ■  Open Source Software Development Kit ■  ROLE Widget Store (role-widgetstore.eu) ■  ROLE Sandbox (role-sandbox.eu)
  • 25. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 25 Learning Layers ROLE Sandbox – Geospatial & Temporal Access §  Users: 5787 (95% external) §  Widgets: 1475 (71.5% external) §  Spaces: 1283 (64.3% external) §  Shared Resources: 18922 (6% external)
  • 26. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 26 Learning Layers ROLE Requirements Bazaar – Community-aware Requirements Prioritization Factors influencing requirements ranking User-controlled weighting of ranking factors Community-dependent requirements ranking lists http://requirements-bazaar.org
  • 27. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 27 Learning Layers Learning Analytics Visualization – Dashboards 1.  Database Selection 2.  Filter Selection/ Definition 3.  Adapted Visualization
  • 28. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 28 Learning Layers LEARNING LAYERS – SCALING UP TECHNOLOGIES FOR INFORMAL LEARNING IN SME CLUSTERS
  • 29. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 29 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
  • 30. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 30 Learning Layers Learning Layers – Scaling Technologies for Informal Learning Learning Layers – Scaling up Technologies for Informal Learning in SME Clusters
  • 31. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 31 Learning Layers Space (shared by multiple users) Using the ROLE Framework for Semantic Video Annotation Web application (composed of widgets) Widget (collaborative web component) http://role-sandbox.eu/
  • 32. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 32 Learning Layers SeViAnno Prototypes SeViAnno (Web) SeViAnno 2.0 (Widgets) AnViAnno (Android) AchSo! (Android)
  • 33. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 33 Learning Layers COMMUNITY LEARNING ANALYTICS – EXPERT IDENTIFICATION
  • 34. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 34 Learning Layers Experts in Learning Communities ■  In learning communities many experts from different fields meet –  Intergenerational learning –  Interdisciplinary learning ■  New Openness for Amateur Contributions ■  Methods, Tools & CoP co-develop –  Expert role models needed –  Expert identification based on complex media traces
  • 35. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 35 Learning Layers YouTell - A Web 2.0 Service for Collaborative Storytelling §  Collaborative storytelling §  Web 2.0 Service §  Story search and “pro-sumption” §  Tagging §  Ranking/Feedback §  Expert finding §  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
  • 36. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 36 Learning Layers Expert Finding – Computation of Actual Knowledge ■  Data vector consists of –  Personal data vector –  Competences, skills, qualification profile –  Self-entered data –  Story data vector –  Visits of stories –  Involvement in projects –  Expert data vector –  Advice given –  Advice received –  Value = #Keywords – Date Decay – Feedback Motivation PESE: Web 2.0 –Anwen- dung für community- basiertes Storytelling Der PESE- Prototyp Evaluierung des Prototypen Zusammen- fassung Ausblick Find the most appropriate expert Data vector represents knowledge of the expert
  • 37. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 37 Learning Layers Knowledge-Dependent Learning Behaviour in Communities Renzel, Cao, Lottko, Klamma: Collaborative Video Annotation for Multimedia Sharing between Experts and Amateurs, WISMA 2010, Barcelona, Spain, May 19-20, 2010 §  Expert finding algorithm: Knowledge value of community sorted by keywords §  Community behavior: Experts spent more time on the services §  Experts prefers semantic tags while amateurs uses “simple” tags frequently §  Community tags: Experts use more precise tags
  • 38. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 38 Learning Layers Threads to Expert Finding ■  Compromising techniques —  Sybil attack [Douc 2002], Reputation theft, Whitewashing attack, etc.. —  Compromising the input and the output of the expert identification algorithm ■  Example: Sybil attacks —  Fundamental problem in open collaborative Web systems —  A malicious user creates many fake accounts (Sybils) which all reference the user to boost his reputation (attacker’s goal is to be higher up in the rankings) Sybil  region  Honest  region   ABack  edges  
  • 39. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 39 Learning Layers Conclusions & Outlook ■  Community Learning Analytics –  Informal learning more challenging for learning analytics –  New research challenges and funding opportunities –  Highly interdisciplinary and multi-method research ■  Case Studies –  Responsive Open Learning Environments – ROLE SDK for Near Real-Time Widget-Based Web Applications –  Learning Layers - Scaling up Technologies for Informal Learning in SME Clusters – Informal Learning on the Workplace – Collaborative Semantic Video Annotation