In virtually all institutions of higher learning, there is the development of online courses to meet local demands foremost but also to potentially glean from an international cadre of learners. Various universities may offer curricular topics relevant to a much larger context. This chapter makes the case that network science may be applied to marketing online courses in higher education to reach target learners. This focuses on some real-world instructional design cases and the marketing strategies used. These cases are then analyzed using a social network strategy approach, which is first described here. Finally, there are ideas on how to apply a social network strategy to various online learning “products.”
TopBackground
While the study of social networks has existed for over 80 years (stemming from sociology), with the first sociograms made in the 1930s, the application of network analysis software has been a fairly recent phenomenon within the past 15 years or so. A basic sociogram consists of node-link diagrams (linegraphs) of points and lines placed on a two-dimensional graph (with an x and y axis). The nodes represent entities (individuals or groups), and the lines between the nodes represent relationships. Sociograms may be studied as single egos (a singular node) and its ego-neighborhood (the central ego and the “alters” / other nodes in its direct-link ego neighborhood). Egos have their own attributes or descriptors which inform their behaviors in the network. Said another way, egos have “biases” or preferences or properties.
Social Network Analysis: Social networks may also be analyzed at other units of analysis: dyads (pairs of nodes); subclusters or cliques; islands (intensely connected groupings in a social network); partitions (parts of a social network), or entire social networks (its content nodes, its relationships, its interconnectivity, the resources moving through it, and other factors). Fat nodes are those which are centralized in a network and receive inputs from other nodes. Its “in-degree” is high (or it has a lot of resources coming in from other nodes). Thin nodes have few ties to other nodes, and they are peripheral to a social network (but they may have power in boundary-spanning or bridging connections between different social networks that do not normally interact). Affiliation (non-kin) networks show how non-family-related people interact in a group, with the most connected members at the center, the less connected ones in the sub-periphery, and then the least connected (like “isolates” or nodes which do not connect to any other member and “pendants” or nodes which are connected to the social network by only one connector) in the periphery (Hanneman & Riddle, 2005).
Social networks are understood to be ad hoc in some respects but also human-created (artificial) in others. People do not connect randomly by happenstance but are drawn to others by shared interests and similarities (termed “homophily” or like attracting like, bonding around similarities). This approach views people as social creatures who create power structures. Heterogeneous networks (with a diversity of node types) are thought to be more capable than homogeneous ones. Those with stronger ties among members are thought to be more resilient (assuming good resources and valid information are moving through the network—instead of negatives like violence or untruths or diseases). People interact, and they share.
One common social network modeling of the world involves “small world networks,” which depict the world as conglomerations of small clusters of close ties between people. There are few degrees of separation between individuals who may not even know each other, which has been highly touted to show high human connectivity. One researcher considers small-world networks a temporal compromise between order and disorder, a way for people to apply sense-making to their social worlds: