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Technical Challenges for Realizing Learning Analytics

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Technical Challenges for Realizing Learning Analytics
Learntec 2015, January 28, 2015, Karlsruhe, Germany,
Ralf Klamma
Advanced Community Informations Systems (ACIS) Group
RWTH Aachen University

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Technical Challenges for Realizing Learning Analytics

  1. 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. Technical Challenges for Realizing Learning Analytics Ralf Klamma Advanced Community Information Systems (ACIS) RWTH Aachen University, Germany klamma@dbis.rwth-aachen.de LEARNTEC, Karlsruhe, Germany, January 27th, 2015
  2. 2. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 2 Learning Layers RWTH Aachen University • 512 professors, 4675 academic and 2443 non-academic colleagues • Annual budget around 884 million Euros, 445 million Euros funded by third parties • 1,250 spin-off businesses have created around 30,000 jobs in the greater Aachen region over the past 20 years •  260 institutes in 9 faculties as Europe’s leading institutions for science and research •  Currently around 40,375 students are enrolled in over 130 academic programs •  Over 6,300 of them are international students hailing from 120 different countries
  3. 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. 4. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 4 Learning Layers Agenda LearningAnalytics CommunityLearningAnalytics ExpertsinCommunityInformation Systems OverlappingCommunityIdentification Conclusions&Outlook
  5. 5. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 5 Learning Layers LEARNING ANALYTICS
  6. 6. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 6 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
  7. 7. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 7 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 High-quality, specially designed, learning materials like books or course material Gaps in personal knowledge identified mostly by real-world practice
  8. 8. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 8 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
  9. 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 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
  10. 10. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 10 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
  11. 11. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 11 Learning Layers Learning Analytics Visualization – Dashboards
  12. 12. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 12 Learning Layers Learning Analytics vs. Community Learning Analytics Formal Learning Learning Analytics Community Regulated Learning Community Learning Analytics Environment LMS EDM/Visual Analytics (VA) – xAPI?? Responsive Open Learning Environment (ROLE) Data Mining / VA / Social Network Analysis / Role Mining Tools Fixed LMS Specific Eco-System Tool Recommender Activities Fixed Content Recommender Dynamic Content Recommender / Expert Recommender Goals Fixed Progress Dynamic Progress / Goal Mining / Refinement Communities Fixed Not applicable Dynamic (Overlapping) Community Detection Use Cases Courses Learning Paths Peer Production / Scaffolding Semantic Networks of Learners / Annotations
  13. 13. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 13 Learning Layers COMMUNITY LEARNING ANALYTICS
  14. 14. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 14 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
  15. 15. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 15 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
  16. 16. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 16 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?
  17. 17. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 17 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
  18. 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. 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. 20. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 20 Learning Layers COMMUNITY LEARNING ANALYTICS – EXPERT IDENTIFICATION
  21. 21. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 21 Learning Layers Space (shared by multiple users) Video-Based Learning Framework Web application (composed of widgets) Widget (collaborative web component) http://role-sandbox.eu/
  22. 22. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 22 Learning Layers ROLE Sandbox – Geospatial & Temporal Access §  Users: 1046 §  Widgets: 523 §  Spaces/Activities: 1377 §  Shared Resources: 3764
  23. 23. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 23 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
  24. 24. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 24 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
  25. 25. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 25 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
  26. 26. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 26 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   A0ack  edges  
  27. 27. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 27 Learning Layers Conclusions & Outlook ■  Learning Analytics for Formal and Informal Learning –  Challenges for data gathering and data management –  Challenges for quantitative and qualitative analysis –  Challenges for visual analytics, feedback and interventions ■  Community Learning Analytics –  Responsive Open Learning Environments (ROLE) –  Learning Layers – Learning Analytics as a Service – Social Network Analysis – Community Detection – Expert Identification