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Harnessing Collective Intelligence in Personal Learning Environments
Harnessing Collective Intelligence in Personal Learning Environments
Harnessing Collective Intelligence in Personal Learning Environments
Harnessing Collective Intelligence in Personal Learning Environments
Harnessing Collective Intelligence in Personal Learning Environments
Harnessing Collective Intelligence in Personal Learning Environments
Harnessing Collective Intelligence in Personal Learning Environments
Harnessing Collective Intelligence in Personal Learning Environments
Harnessing Collective Intelligence in Personal Learning Environments
Harnessing Collective Intelligence in Personal Learning Environments
Harnessing Collective Intelligence in Personal Learning Environments
Harnessing Collective Intelligence in Personal Learning Environments
Harnessing Collective Intelligence in Personal Learning Environments
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Harnessing Collective Intelligence in Personal Learning Environments

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Presentation at ICALT 2012

Presentation at ICALT 2012

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  • 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. Overview Personal Learning Environments (PLEs) Knowledge Overload Social Filtering A Service for Personal Learning Management (PLEM) 2
  • 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. 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. 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. From Scarcity to Abundance PLE: From knowledge‐push to knowledge‐pull Abundant access to information Knowledge Overload Need for knowledge filters 6
  • 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. 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. 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. 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. 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. 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. Thank You chatti@cs.rwth-aachen.de mohamedaminechatti.blogspot.com13

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