NetLearn: Social Network Analysis and Visualizations for Learning

NetLearn : Social Net work Analysis and Visualizations for Learn ing Mohamed Amine Chatti , Matthias Jarke, Theresia Devi Indriasari RWTH Aachen University, Germany Marcus Specht Open University Heerlen, Netherlands ECTEL 2009 Nice, October 1, 2009

Agenda
Personal Learning Environments
Community mining & Expertise finding
Social Network Analysis and Visualizations
NetLearn

Personal Learning Environments
The environment in which I learn
A more natural and learner-centric model to learning
Put the learner at the center and give her control over the learning experience
Convergence of lifelong, informal and network learning within a learner-controlled space
Lifelong Learning Informal Learning Self Organized Learning Network Learning Personal Learning Environments Pedagogical Perspective

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 knowledge Symmetric relationships Knowledge-push Knowledge-pull

From Scarcity to Abundance
PLE: From knowledge-push to knowledge-pull
Get knowledge to learners
Knowledge overload
Need for filters to help learners find quality knowledge nodes
Explicit knowledge (information) vs. Tacit knowledge (people) (Nonaka&Takeuchi, 1995)
Need for community/network mining and expertise finding mechanisms
(Seely Brown , 1999)

Social Network Analysis & Visualizations
Social Network Analysis (SNA) is the quantitative study of the relationships between individuals or organizations (Wasserman&Faust, 1994)
A Graph G = (V , E)
where V = {1, 2, …., n} is a set of nodes (vertices)
E ⊆V x V is a set of edges (arcs, links, ties)
Centrality measures: degree, closeness, and betweenness centrality
Social Networks Visualization
Node-link diagrams
Matrix-based

Network Characteristics: Degree centrality
The degree centrality of a vertex v ∈ V is simply the degree of that vertex
Degree of a vertex: number of incoming and outgoing edges
in-degree
out-degree
The degree centrality finds the actor with the most influence over the network (popularity of an actor, connector, hub)
C D (Fernando) = 6

Network Characteristics: Closeness centrality
Closeness centrality is defined as inverse closeness, i.e., the sum of the distances (shortest paths) to all other vertices
Closeness centrality focuses on how close an actor is to all the other actors in the network (finds actors with the best visibility into what is happening in the network)
C c (Fernando) = 1/15 C c (Andre) = C c (Jane) = 1/14

Network Characteristics: Betweenness centrality
Betweenness centrality is defined as the sum of the fractions of shortest paths between other actors that an actor sits on
Betweenness centrality finds actors that control the information flow of the network (broker node in the network, great influence over what flows – and does not – in the network)
C B (Fernando) = 1/3 + 1/3 = 0.66 C B (Carol) = 2 x (1/1 + 1/1 + 1/1 + 1/1 + 2/2 + 1/1 + 1/1) = 14

NetLearn
NetLearn: Social Net work Analysis and Visualizations for Learn ing
Applying social network analysis and visualizations methods for community mining and expertise finding
Case Study:
Co-authorship Network
1000+ TEL researchers
New bibliography entries via a Plone-based interface
Keywords either manually entered or automatically generated using the ALOA Framework (Chatti et al., 2008)

NetLearn Design

NetLearn Implementation
Author Mining Module
Visualization of the global co-authorship network
Node: author; Edge: co-authorship
Interactive browsing, Different layouts
Edge betweenness clustering
Computation of centrality statistics
Reflection of the Long Tail phenomenon
Keyword Mining Module
Mining communities around specific keywords
A keyword community is a cluster densely connected by the same keyword
Node: author; Edge: shared keyword
Community Mining Modules

NetLearn Implementation
Local Author Module
Ego-centric network of an author
Keyword Community Module
Expertise finding based on keywords
Locating researchers working on a specific topic or topics closely related to that topic
Graph – chart – community – table views
Interest Community Module
Expertise finding based on query occurrence in title, abstract, keyword
Referral Chain Module
Chain between two researchers
Shortest path algorithm
Expertise Finding Modules

NetLearn Demo

Thank You!

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NetLearn: Social Network Analysis and Visualizations for Learning

by Mohamed Amine Chatti on Oct 06, 2009

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