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

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

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

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)

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

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

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

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)

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

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)

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

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)

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: 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

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

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

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

NetLearn Demo

Thank You!