Harnessing Collective Intelligence in Personal Learning Environments

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Harnessing Collective Intelligence in Personal Learning Environments

  1. 1. Harnessing Collective Intelligence in  Personal Learning Environments Mohamed Amine Chatti, Ulrik Schroeder, Hendrik Thüs, Simona Dakova Informatik 9 (Learning Technologies), RWTH Aachen University1
  2. 2. Overview Personal Learning Environments (PLEs) Knowledge Overload Social Filtering A Service for Personal Learning Management (PLEM) 2
  3. 3. Personal Learning Environments (PLE) Pedagogical Perspective The environment in which I learn A more natural and learner‐centric  Lifelong Learning Informal Learning model to learning  Put the learner at the center Personal Learning Environments Network Learning PLE: Convergence of lifelong,  Self‐Organized Learning informal, network, and  personalized learning 3
  4. 4. Personal Learning Environments (PLE) Technical Perspective PLE: Self‐defined  collection of  services, tools and  devices that help  learners build their  PKNs and learnPersonal Knowledge Network (PKN):• Tacit Knowledge Nodes (People)• Explicit Knowledge Nodes (Information)
  5. 5. LMS vs. PLE LMS PLE Content-centric Learner-centric Management Sharing Pre-defined selection of tools Learner needs first, tool selection second One-size-fits-all Personal, responsive Formal learning Support Informal and lifelong learning support Centralized, closed, bounded Distributed, loosely coupled, open Structured, heavyweight, rigid Freeform, lightweight, flexible Top-down, hierarchical Bottom-up, emergent Command&control, one-way flow of Symmetric relationships knowledge Knowledge-push Knowledge-pull5
  6. 6. From Scarcity to Abundance PLE: From knowledge‐push to knowledge‐pull Abundant access to information Knowledge Overload Need for knowledge filters 6
  7. 7. Knowledge Filters Knowledge Filters  Personal Network  Recommender Systems  The Wisdom of Crowds / Collective Intelligence  None of us is smarter than all of us [Surowiecki, 2004] 7
  8. 8. PLEM PLEM: A Social Software for Personal LEarning Management Goal: Harnessing collective intelligence to locate quality knowledge nodes (learning resources, services, experts)  Social interaction metrics (e.g. Facebook, Twitter, Digg, Delicious) 8
  9. 9. Ranking in PLEM Ranking of learning elements based on social interaction metrics Idea:  Consider each simple interaction with a learning element as a vote  The learning element that gets the most votes goes first on the listsubprogra.informatik.rwth-aachen.de:8180/PLEM/ 9
  10. 10. User Evaluation Online Questionnaire  22 evaluators  Usability Evaluation  Subset of the 50‐question database of the Software Usability  Measurement Inventory (SUMI)  Evaluation using the System Usability Scale (SUS)  Average user satisfaction of 66 points out of 100 points  Needs improvements in terms of system learnability and user interface  Functionality Questions (ranking quality) 10
  11. 11. Future Work: Recommendation Techniques Examples Memory‐based algorithms:  Neighborhood‐based CF Collaborative  Top‐N recommendation Model‐based algorithms:  Machine learning / data mining  algorithmsContent based  Information retrieval  Hybrid  Combination of collaborative and  content‐based approaches 11
  12. 12. Recommendation in PLEM Memory-based tag-based CF K Nearest neighbourAnalysis Offline and Evaluationadaptation Model-based User of tag-based CF Evaluationalgorithms Dimension reduction Classification Clustering Association 12
  13. 13. Thank You chatti@cs.rwth-aachen.de mohamedaminechatti.blogspot.com13

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