In my previous post, I walked a long way around to finally say that one of the main reasons I like Connectivism is because it seems to be a vibrant conversation within the context of a much larger conversation about complex network structures. If Morin and Sporns are correct, then science in general is moving away from the closed-system, reductionist science of the past to an open-system, complex science of the future. From my point of view, the people engaged in the conversation heating up about Connectivism are moving in a direction that is complementary to this larger shift. I want to be part of that shift.
However, Sporns' book has opened my eyes to what I suspect may be a gap in the Connectivism community: quantitative research. In the Introduction to his book Networks of the Brain (2011), Sporns points out that "connectivity comes in many forms—for example, molecular interactions, metabolic pathways, synaptic connections, semantic associations, ecological food webs, social networks, web hyperlinks, or citation patterns" (1), and I assume that we can include students and teachers in traditional classrooms, MOOCs, and PLNs. He points out, however, that all these different kinds of connectivity require hard scrutiny from the perspective of network science. He says: "In all cases, the quantitative analysis of connectivity requires sophisticated mathematical and statistical techniques" (1).
Perhaps I have overlooked this pocket of discussion, but I think Connectivism lacks a strong, quantitative voice. I know that I don't have that voice, but I think all our conversations could be a bit more grounded if they were informed from time to time with precise observations and quantifiable measurements. Now, I'm an English teacher, so I am not suggesting that quantitative analysis is the only answer, but it is certainly part of the answer.
What might we investigate quantitatively? Sporns suggest several lines of research for neuroscience that might be enlightening for those of us in education in general and composition in particular. For instance, he notes that "nervous systems are composed of vast numbers of neural elements that are interconnected … [thus, we can] probe for architectural principles that shape brain anatomy" (3). Similarly, MOOCs are composed of vast numbers of people and resources that are interconnected by computer networks; thus we can probe for architectural principles that shape a MOOC's anatomy. The writing specialist might phrase it this way: MOOCs are composed of vast numbers of documents from blog posts, to essays, to Elluminate sessions, to tweets that are propagated and interconnected by computer networks; thus, we can probe for architectural principles that shape the written and recorded conversation.
This last question, of course, is of real interest to me, and I am confident that I could tie any findings back into the scholarly conversation about writing, but I am not confident that I have the "sophisticated mathematical and statistical techniques" to generate those findings. Still, this MOOC is sitting here with all these linked documents. It's a shame to see it go to waste. Anybody with sufficient quantitative skills (and I don't even know enough to know what those are) want to join me in this research? Or is this research already underway? Anyone?
PS: The ink was not dry on this post when I happened on a post by Sui Fai John Mak about quantitative research into Connectivism and MOOCs. He references some studies by Dave Cormier, Rita Kop, and others, which shows that this conversation has been heating up. So it was there all along, and I just didn't hear it until I started thinking about it. Seems that's the way my brain works.
PS. Maybe that's the way the network works! ;)ReplyDelete