Learning Analysis of Social Networks

Learning Analysis of Social Networks:

This is my post of social network analysis for learning. we have a tendency to square measure beginning with the thought that there's worth in understanding however interactions happen throughout learning in spite of the context. This sets the stage for excavation in deeper and conducting analysis on the social networks that learners participate in like twitter or a diary.Dragan (our instructor) mentioned that researchers have typically thought social networks is also the foremost necessary part of learning. and also the analysis of social networks is predicated on numerous analysis fields. He mentioned some key characteristics which will be of focus embody density, centrality, and modularity.

Network components:

Social networks have some key structural components which will be known so as to ascertain a typical language and abstract model. this enables USA to research them. during this mooc we have a tendency to mentioned 3 key components, the actor, relations, and knowledge sources.

Actor:

The actor may be a node or vertex among the network. In social networks this can be usually an individual or learner, however I don’t assume it might essentially have to be compelled to be an individual.

Relations:

The relation within the network refers to the ties, edges, arcs, and links that connect the actors. Relations may be planless and weighted or they will have a direction, that means that associate actor may be the sender and any actor that receives knowledge may be the receiver. So, actors may be senders or receivers or each. in addition, the relation between 2 actors also can be labeled or categorised. this implies they will represent one thing, like friendly relationship, advice, hindrance, or may be a kind of communication. i'd imagine this might be a awfully attention-grabbing part of network analysis to do and establish and outline these relations for the aim of understanding the learner, the network, or the context.

Data Sources:

The third component mentioned within the grub was the realm of however the information was collected. i feel the thought of however you gather your knowledge can have an impression on what filter you utilize to research the information. Is it you own knowledge like email or is it from twitter? This assortment method impact what you'll} examine and {make|and build} a possible bias on however you analyze the data and what conclusions you will be able to make from that analysis.

As a mirrored image, I don’t assume this third component is well articulated among the mooc materials.

Analysis:

There we have a tendency tore 3 key square measureas of study that we are gazing that embody density, centrality, and modularity.

Density is that the degree to that actors square measure connected to all or any the opposite members within the network. position is that the extent within which the actors square measure organized around a central purpose.

Modularity is that the means that you just quantify the modules among the network or community, by enumeration and analyzing the ties between the actors. (It will get a lot of sophisticated quickly, however this can be the core.)