I’m familiar with network analysis, especially in social sciences research, so most of the concepts from Week 2 of CCK2011 made sense. Different words (nodes versus vertices) are used in different fields to describe the same basic ideas, and once I got a handle on that I was fine with the readings and discussion. In some contexts, though, I’ve noticed that writers tend to discuss networks as if they are static, whereas I see them as incredibly dynamic and constantly mutating. The challenge is to work this into your analytic tools, so that the diagram of your network changes as the network fluctuates and re-forms. (Something akin to Hans Rosling’s reworked bubble charts, that show change over time [obviously he’s not talking about networks; I’m just interested in the illustration of time in a chart].)
Research companies I write and edit for have discovered important insights by using network analysis (e.g., how health knowledge can purposefully filter through a particular community). Where real people in physical proximity are concerned, I get it.
Where machines and online personas are concerned, however, I am somewhat unsure.
Here’s one example: we don’t let my preteen stepdaughter “friend” anyone on Facebook that she doesn’t know in “real life.” Still, I get a little cranky when she announces to her 122 “friends” that we’re going out of town for the weekend and that we bought a new flat screen television. Who else are her friends are connected to that I don’t know? How many other people use the same physical computer as her friend? There are nodes in this network of which I am completely unaware (though clearly I “sense” their presence).
How to account for those anonymous nodes and their influence on the network and the other nodes within it? I’ll put aside for the moment the question of Those Nodes That Mean Me Deliberate Harm—that’s sort of an extreme example. If knowledge is connections, then it seems that a catalyst of learning (as vjansen describes in a course discussion thread) could be the activation of an anonymous node (you could take this to the Rupert Sheldrake level, too, or talk about the family constellations work of Bert Hellinger). For instance, my husband is an eighth-grade science teacher, and his stories about his students viscerally tamp down my enthusiasm for the Amazing, Uplifting, Astounding Potential of Technology in Education a bit because of the pressing issues he deals with on a regular basis that interfere with students’ learning (say, unplanned pregnancies at age 13).
Another example might be the anonymous machine nodes that collect personal information in order to sell me products I might be interested in based on my past purchases (“you might also like…”). They’re mostly hidden to me but certainly their influence is there: on Amazon, I see one book and not a different one in this section, and I might read it and then recommend it to a friend or colleague in my network. That hidden (even to me) node affects my colleague directly and potentially her colleagues.
George Siemens writes
In a networked world, the very manner of information that we acquire is worth exploring. The need to evaluate the worthiness of learning something is a meta-skill that is applied before learning itself begins. … The ability to synthesize and recognize connections and patterns is a valuable skill. (Siemens 2005)
What does connectivism do with these anonymous network nodes? Does evaluating something’s “worthiness” rest on the premise that all of our connections are known to us and the network? Couple this with the idea that those networks are in constant flux, and analyzing how learning happens gets even trickier.