Community Learning Analytics - Challenges and Opportunities - ICWL 2013 Invited Talk
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Community Learning Analytics – Challenges and Opportunities
Invited Talk ICWL 2013, Kensing, Taiwan, October 7, 2013
Ralf Klamma
Advanced Community Information Systems (ACIS)
RWTH Aachen University, Germany
klamma@dbis.rwth-aachen.de

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Community Learning Analytics - Challenges and Opportunities - ICWL 2013 Invited Talk Presentation Transcript

  • 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 – Challenges and Opportunities Ralf Klamma Advanced Community Information Systems (ACIS) RWTH Aachen University, Germany klamma@dbis.rwth-aachen.de ICWL 2013, Kensing, Taiwan, October 7, 2013
  • 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 RWTH Aachen University • 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” •  260 institutes in 9 faculties as Europe’s leading institutions for science and research •  Currently around 38,000 students are enrolled in over 130 academic programs •  Over 5,000 of them are international students hailing from 120 different countries http://www.rwth-aachen.de/cms/root/Die_RWTH/Profil/~enw/Daten_Fakten/lidx/1/
  • 4. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 4 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
  • 5. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 5 Learning Layers Agenda LearningAnalytics CommunityLearningAnalytics LearningLayers Conclusions&Outlook
  • 6. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 6 Learning Layers LEARNING ANALYTICS
  • 7. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 7 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
  • 8. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 8 Learning Layers The long tail of personal knowledge in life-long learning   Zillions of new learning opportunities   Abundance of learning materials   But: Extremely challenging to find & navigate –  Learning Analytics or Educational Data Mining High-quality, specially designed, learning materials like books or course material Gaps in personal knowledge identified mostly by real-world practice
  • 9. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 9 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 Learning Management System 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
  • 10. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 10 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
  • 11. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 11 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
  • 12. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 12 Learning Layers ROLE Approach to the Design of Learning Experiences How can we enable und exploit Learning Analytics for 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 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)
  • 14. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 14 Learning Layers ROLE PLE Sandbox & SDK Space (shared by multiple users) Web application (composed of widgets) Widget (collaborative web component) http://role-sandbox.eu/
  • 15. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 15 Learning Layers Learning Analytics Visualization – ROLE Dashboards
  • 16. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 16 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
  • 17. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 17 Learning Layers COMMUNITY LEARNING ANALYTICS
  • 18. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 18 Learning Layers Learning Communities Communication / Cooperation ? Cultural heritage in Afghanistan Database Content input / request Content retrieval Surveying/ safeguarding Sketch drawing Photographing Surveying/ recording GPS positioning Experiences imparting Administration UNESCO Teaching/ presentation Asia ICOMOS Standards defining Research RWTH Aachen SPACH www.bamiyan-development.org
  • 19. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 19 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?
  • 20. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 20 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
  • 21. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 21 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
  • 22. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 22 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 manage- ment 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
  • 23. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 23 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 PLE/Community Information Systems Web 2.0 Processes (i*) (Structural, Cross-media) Members (Social Network Analysis: Centrality, Efficiency, Community Detection) Network of Artifacts Content Analysis, Sentiment Analysis, Tiopic Mining, Goal Mining, Social Network Analysis Network of Members Communities of practice Media Networks
  • 24. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 24 Learning Layers Community Learning Analytics in CoP   User-to-Service Communication •  Identification of successful CoP services •  Identification of CoP service usage patterns   User-to-User Communication •  CoP-aware Social Network Analysis •  Identification of personal learning activities/goals/patterns etc. •  Identification of expert CoP members •  Identification of overlapping communities +
  • 25. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 25 Learning Layers Supporting Community Practice with the MobSOS Success Model
  • 26. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 26 Learning Layers Community SRE Processes– i* Strategic Rationale
  • 27. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 27 Learning Layers ROLE Requirements Bazaar – Community-aware Requirements Prioritization Factors influencing requirements ranking User-controlled weighting of ranking factors Community-dependent requirements ranking lists
  • 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 Learning Layers   Large scale integrated project on scaling up informal workplace learning   Objectives –  Support informal learning –  Unlock peer production –  Scaffold meaningful learning   Two regional clusters –  Construction (Germany) –  Healthcare (UK) 29 http://learning-layers.eu/
  • 30. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 30 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
  • 31. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 31 Learning Layers Learning Layers – Scaling up Technologies for Informal Learning in SME Clusters Learning Layers – Scaling Technologies for Informal Learning
  • 32. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 32 Learning Layers Collaborative Annotation of Peer-Produced Video 32
  • 33. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 33 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
  • 34. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 34 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
  • 35. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 35 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  
  • 36. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 36 Learning Layers Conclusions & Outlook   From Web 2.0 Knowledge Management to Personal Learning Environments   ROLE - Responsive Open Learning Environments –  Enabling Learning Analytics in Personal Learning Environments   Learning Layers - Scaling up Technologies for Informal Learning in SME Clusters –  Informal Learning on the Workplace –  Community Learning Analytics on a Large Scale –  Collaborative Semantic Video Annotation