Saturday, February 9, 2013

Why Rhizomatic Learning? #etmooc

Okay, so I enjoyed the conversation about rhizomatic education over at Christina Hendricks' blog, You're the Teacher. In the conversation, I'm definitely championing rhizomatic, connectivist education, but why? I've been writing about this for a couple of years now, but can I state my point of view succinctly and reasonably clearly? Well, I can try.

Learning is a network phenomenon.

That's rather succinct, and owes deep apologies to neuroscientist Olaf Sporns, but I can say it with a bit more texture: learning is a function of our complex interactions across multi-scale physical, cognitive, technological, and social networks. For me, this is the DNA of a connectivist and rhizomatic view of learning, and everything else I say about learning will follow from this core idea. At least, I hope so.

But can I defend my claim that learning is a network phenomenon? I think so, but in some ways, starting points always carry with them assumptions that one either accepts or doesn't, and they carry assumptions that the believer is quite often unaware of. I think my use of networks falls into this category. However, I can point to some reasons why I use the concept.

Networks provide me a most useful model of how the Universe/Reality/Everything works, including learning. Of course, as soon as I say that, I am reminded of George E. P. Box's famous dictum that "essentially, all models are wrong, but some are useful" (Empirical Model-Building and Response Surfaces, 1987). I am convinced that, despite how right network thinking feels to me, eventually people will come to see the faults with the network metaphor just as we are coming to see the faults with the mechanistic clockwork metaphor that we inherited from Galileo, Newton, and Descartes. As Edgar Morin has pointed out in his book On Complexity (2008), the mechanistic, clockwork model of reality and the science and technology built upon it has been spectacularly successful, but over the past century, cracks have begun to appear as we have come to see more of Reality, especially at the macro and micro levels. As we peer into our scopes, bits of reality emerge that no longer fit the clockwork model. Reality is stubborn, so we change our model. But slowly, sometimes too slowly.

The model that appears to be replacing the mechanistic clockwork model is networking. Of course, not everyone uses that term. Edgar Morin speaks of systems, especially complex systems. In his book Interaction Ritual Chains (2005), Randall Collins defines the core sociological unit not an individual but the situation, a dynamic nexus of intersecting vectors which to my mind requires a network structure. James Lovelock calls it Gaia, the movies call it The Matrix. All of these sources have valid reasons for using the term that they do, but to my mind, networking (and here I'm using the verbal form intentionally to capture the complex dynamics in my concept) is the most convenient and natural-feeling term. I spent many years of my professional life building campus networks and connecting students, faculty, and staff to the Internet, so it just works for me; however, I also frequently use the term rhizomatics or rhizomics to play off Deleuze and Guattari's concept of the rhizome (A Thousand Plateaus, 1988), a more free-form, complex, dynamic, and inclusive form of networking that reveals some properties that I find particularly useful and fun.

So the networking model in all its various iterations and apellations appears to be the emerging model of how things work. I like Olaf Sporns' comments about this in his book Networks of the Brain (2011), so I'll end this post with a long quote that has a decidedly scientific bias that I think will inform our thinking in the humanities:
Over the last decade, the study of complex networks has dramatically expanded across diverse scientific fields, ranging from the social sciences to physics and biology. This expansion reflects modern trends and currents that have changed the way scientific questions are formulated and research is carried out. Increasingly, science is concerned with the structure, behavior, and evolution of complex systems such as cells, brains, ecosystems, societies, or the global economy. To understand these systems, we require not only knowledge of elementary system components but also knowledge of the ways in which these components interact and the emergent properties of their interactions. (1)
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