Presentation at ICALT 2012

1. Harnessing Collective Intelligence in
Personal Learning Environments
Mohamed Amine Chatti, Ulrik Schroeder, Hendrik Thüs, Simona Dakova
Informatik 9 (Learning Technologies), RWTH Aachen University
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2. Overview
 Personal Learning Environments (PLEs)
 Knowledge Overload
 Social Filtering
 A Service for Personal Learning Management (PLEM)
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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
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4. Personal Learning Environments (PLE)
Technical Perspective
PLE: Self‐defined
collection of
services, tools and
devices that help
learners build their
PKNs and learn
Personal 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-pull
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6. From Scarcity to Abundance
 PLE: From knowledge‐push to knowledge‐pull
 Abundant access to information
 Knowledge Overload
 Need for knowledge filters
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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]
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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)
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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 list
subprogra.informatik.rwth-aachen.de:8180/PLEM/
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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)
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11. Future Work: Recommendation
Techniques Examples
Memory‐based algorithms:
 Neighborhood‐based CF
Collaborative
 Top‐N recommendation
Model‐based algorithms:
 Machine learning / data mining
algorithms
Content
based
 Information retrieval
Hybrid
 Combination of collaborative and
content‐based approaches
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12. Recommendation in PLEM
Memory-based
tag-based CF
K Nearest neighbour
Analysis
Offline
and Evaluation
adaptation Model-based
User
of tag-based CF
Evaluation
algorithms Dimension reduction
Classification
Clustering
Association
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13. Thank You
chatti@cs.rwth-aachen.de
mohamedaminechatti.blogspot.com
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