Sunday, February 10, 2013

Why Rhizomatic Learning, Pt. 2 #etmooc

In my last post, I said that networking is the lens through which I see most everything, or at least I try. I confess that I still have some old habits of mind, mostly that I'm unaware of, but when brought to mind, I do try to address them. I quoted Olaf Sporns comments that science is increasingly using the networking metaphor to guide both its subject matter and its research. I want to comment on a couple of points he made, and I quote:
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)
Note first that he although he started with the term networks, mid-stream he switches to complex systems, the same term that Edgar Morin uses. In my reading, these terms have often been used interchangeably, and I will likely do so in my own discussion, unless I find some reason to distinguish between them.

Second, Sporns captures neatly the distinction between the reductionist, mechanistic clockwork type of science with its focus on "knowledge of elementary system components" and the evolutionary, organic networking type of science with its focus on "knowledge of the ways in which these components interact and the emergent properties of their interactions." This shift in metaphors, or paradigms if you prefer, is extremely important for me.

Although Sporns and I do not share similar disciplines—he studies and teaches neuroscience and I study and teach writing and literature—his work has a critical, core benefit for me: Sporns insists and demonstrates through exhaustive research that cognition is a network phenomenon. I accept his argument, as I have not found a better, more detailed, more precise description of how the brain works. I was pleased, then, to read James Zull's educational book The Art of Changing the Brain (2002) which applies the networking paradigm to learning and draws out some implications for teaching and pedagogy. Networking, of course, is at the heart of Connectivism. Throughout his writings, Stephen Downes makes a number of statements that express knowledge and learning as network phenomena. For instance, in the 2011 post What Networks Have In Common, Downes says, "the state we call 'knowledge' is produced in (complex) entities as a consequence of the connections between and interactions among the parts of that entity." I could no doubt find even more pointed pronouncements in Downes' writing, but this is sufficient. In his online book Knowing Knowledge (2006), George Siemens says, "Knowing and learning are today defined by connections ... connectivism is the assertion that learning is primarily a network-forming process" (15).

For me, then, learning is the ability of an entity to recognize, build, and traverse networks. Moreover, the tools entities use to recognize, build, and traverse networks are themselves networks, and knowledge is an emergent property of the interactions among and across those networks of neurons, sensory organs, sound waves, light waves/particles, classrooms, social groups, languages, the Universe … as far out or in as you wish to take it.

Anyone who has read Downes and Siemens will see that nothing I say here is new. Those fellows have already said it, and in general, I agree with them (details are always problematic, but that doesn't concern me here). Learning is networking across multi-scale networks, and that has huge implications for the way we teach, but is learning rhizomatic? Perhaps a better way to ask this question is what does the concept of the rhizome as developed in Deleuze and Guattari's book A Thousand Plateaus bring to connectivism that it doesn't already have? This is similar to a question I have heard Siemens ask of Cormier in some of our previous MOOCs, and it merits an investigation, if not an answer. I'll try to do that. Tomorrow.

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