Competency 3.1: Define social network analysis and its main analysis methods.
Social Network Analysis (SNA) provides
insights into how different social processes unfold while learning
happens in any learning environment. It helps us to study the effects of
interaction and social context in education. The different network
elements are actors and their relations. Social Network Analysis is defined as the mapping of connections between
the nodes or actors (peoples) , communities (Organizations) and etc
based on few measures.
The
nodes/ actors could be students email
addresses, tweets or any such actions. I would typically use SNA to see
the
interaction between students, for example in a chatroom/ discussion
forum, to see who is talking to whom, who replies to whom, who is
following what
question, who voted for a question etc. Based on the interaction
patterns, we
can construct the network graph. We can from here see if any measure
from the
network can correlate to learning or performance.
Some measures in SNA for analysis are below:
Diameter:
Diameter determines the
longest distance between any pair of nodes in a network. It measures the
extent to which each individual node can communicate with any other
node in the network.
Density:
Density determines the potential of the entire network to talk to each
other. It can be used to determine the extent to which some individual
nodes share the information. The spread of information is very fast in a
highly dense network.
Degree Centrality:
Degree centrality is a simple measure that indicates the overall number
of connections for each actor in a network. Network measures may have
specific meaning when considered in the context of directed graphs.
In-Degree Centrality:
In-degree centrality is a measure of the number of other nodes that
directly try to establish connection to a particular node. Also refers
to the popularity or prestige of a node in a network.
Out-Degree Centrality:
Out-degree centrality is the measure of the number of nodes to which particular nodes are talking.
Betweenness Centrality:
Betweenness centrality indicates the ease of connection with anybody
else in the network, in particular, to try to connect all small sub
communities in the network. Brokerage role is best measured by this
measure.
Closeness Centrality:
Closeness centrality measures the ease or the shortest distance of a
node to anybody else in the network. It indicates how quickly a node can
get to another node in the network.
Network Modularity:
Network modularity is used to identify common sub-groups talking to each
other where a group of actors have close ties to each other. An
algorithm for finding the giant component can be used to identify the
largest component of all connected nodes in the network. This filters
out single nodes that are not connected to the network to easily
identify and analyse communities in the network.
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