Network Visualization and Personal Learning Networks

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I have written previously about connected learning and how to create a PLN (Personal Learning Network). This kind of learning is learning-on-demand, and it’s an amazing place for students to be. Yet, students need to able to actively and critically locate and handle the information made available by the Internet and social technologies. One way of entering such critical literacy might be through network visualization.

A Little Bit of Background
Situated within the connectivist paradigm, networked learning is an emerging perspective that aims to understand learning processes by asking how people develop and maintain a web of social relations for their learning and professional development. Networked learning focuses on the diversity of social relationships people develop, what strategies they use to maintain them, and the value this creates for learning.

A networked perspective encompasses more and different relations, looking at the diversity of social relationships people maintain and the diversity of relationships (weak to strong) that make up communities and other forms of social networked structures. Therefore, networked learning investigates the number and range of contacts one has and the intention in which they are being used for learning.

Rajagopal et al. (2012) define a personal learning network as “a network set up by an individual specifically in the context of her professional activities through online platforms to support her professional non–formal learning needs.”  Ann Hill Duin and Joseph Moses (2015) introduce personal learning networks as “a collection of people, information resources, organizations, and other connections that a networked individual values because the connections support and contribute to learning interests” and “a means of operationalizing culture in social contexts by making visible learners’ cultural orientation to knowledge, information, and learning.” Within the networked learning culture, a professional who intentionally builds, maintains, and activates her diverse relationships with contacts within her personal network for the purpose of improving her learning—and uses technology to support this activity—is creating a personal learning network.

Visualizing Networks
Networks are powerful when they are represented accurately through visualizations. The primary concern in the design of network visualization is the purpose it is meant to serve (Ognyanova, 2015). Different network goals (emphasizing key actors and links, relational strength, directionality, diffusion patterns, communities, etc.) require different structural properties to be highlighted.

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Graphs (above) are the most common visualization for networks but they are far from the only option for visualization; other network representations may be appropriate for specific cases (below).

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There are theoretical differences between networks and other ways of representing relationships such as phone books, contact lists, book taggings, or even group organization charts. First, a connectivist approach to visualization assumes no leadership or ownership in the structure (Onlinesapiens, 2011). Networks are open without a brand name or dominant proprietor. Groups, on the other hand, are closed and organized, as participation is often subscribed. For instance, Twitter is a huge network while hashtags are groups within the network. Similarly, Facebook is a network while Facebook Groups and Pages are groups.

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Second, visualized networks provide observations of relationships that are invisible through representations like phone books and book tags. The edges and nodes that make up the connections between entities show more than just their existence and metadata but also properties such as relationship strength, frequency, centrality, and directionality through various rendering of these connections (see above).

Third, whereas tags and lists are linear and static, networks are dynamic, multidimensional, and always growing or expanding. Content within a network can spread and create a ripple effect when one or more nodes are impacted. This interdependency between nodes can also be possible between networks––a system of coupled networks where nodes of one or more networks depend on nodes in other networks. 

In the context of learning, networks can be a powerful visualization technique for representing the relationships between current information resources and envisioning connections with future sources of knowledge. Networks can be a research strategy for users to locate their sites of information and inquiry, and for them to visualize the development of their work. For professionals, networks are a living and growing element of the workplace from which they also discover new directions or dimensions in their respective careers.

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