Wednesday, January 2, 2013

Rewiring the Neuron

My good friend Bruce recommended that I read James E. Zull's book The Art of Changing the Brain (2002), and I'm glad that I followed his advice. The book has some important implications for connectivist, rhizomatic thinking.

The first section of the book establishes a direct correlation between brain form and functions and teaching and learning. As Zull says in the very first sentence: "Learning is about biology." For Zull, learning is the process of changing neuronal structures, and good teaching is aware of and works with the brain's innate structures and functions to enable those changes. I'm a bit uneasy about reducing learning to physical changes in the brain, but I can accept this as a useful focus for better understanding this aspect of learning. So what does this connection between brain structure and learning imply for connectivism and rhizomatic learning?

First, Zull understands the brain as a network. This is implicit in the first part of his book, but he later makes it explicit, devoting Chapter 6 to an exploration of neuronal networks. Moreover, he conceives the brain as a multi-scale, complex networking structure. For instance, individual neurons are networked to process certain sensory inputs, but then those individual networks are networked into larger networks to perform various integrative and meaning-making functions. Zull does not extend those networks into the larger networks of the body or our social and natural ecosystems, but that may be a result of his particular focus for this book rather than a specific belief. I find nothing in what he says that would exclude such an extension of our neuronal networks. It's easy for me to say, then, that Zull, like connectivists, sees learning as a network phenomenon. For Zull, learning is the process of shaping neuronal networks that map more or less well to reality. This process of neuronal mapping is quite compatible with Deleuze and Guattari's notions of decalcomania and cartography, and network structures are at the heart of both connectivism and the rhizomatics.

Zull provides some specifics about neuronal processes that are useful for connectivism and rhizomatics, that make cartography and decalcomania more practical. First, he outlines the basic sequence of learning:

  1. sensing,
  2. integrating (2 parts), and
  3. acting.
For Zull, then, learning is grounded in sensing the physical world, integrating those sense impressions into our existing neuronal networks first through reflection and then abstraction, and then acting on, or testing, that new knowledge. This is a dynamic, complex process because the results of acting/testing is then fed back into the loop as we sense the consequences of our actions/tests, integrate those consequences, and act/test again. Through this process, our brains develop themselves, strengthening those neuronal networks that lead to somehow satisfactory results and weakening those networks that lead to unsatisfactory results. Of course, the loop is not this simple. We humans are often confused about which neuronal networks map satisfactorily to reality and which don't, and our brains can create and adhere to some awful mappings, but mostly it seems to work for us, and according to the latest neuroscience, this is the process most of us use.

The takeaway for me is that the educational process is improved if it begins with a concrete, sensory experience, includes time for reflection and abstraction of the experience, allows for action/testing of the new knowledge, and allows for feedback of the testing results into the loop. A teacher doesn't need to know anything about connectivism or rhizomatics to follow this teaching process, but this process can usefully inform connectivism and rhizomatics.

First, at the start of any lesson, students favor concrete, sensory experience. This is what they are learning. Unfortunately, that means most of our students are learning whether or not they like their teachers, whether or not the teacher is boring, whether or not the classroom is comfortable, whether or not this class fits into their eating schedule, and so forth. Our brains are wired this way, and our brilliant lectures can seldom overcome this sensory bias. Perhaps, then, we should capitalize on the sensory bias, and begin there. For instance, in my writing classes, I could start by having my students text someone on their smartphones about what they are doing in my class, and then launching into a classroom discussion of why people write to each other.

Second, students need time to integrate their new sensory experiences into their existing neuronal networks. Implications: if we march tenaciously through the content, we will lose most students. At best they will manage to register the content into short-term memory, which is a necessary step in learning, but hardly sufficient for any useful learning. The brain quickly flushes short-term memory to make room for the next short-term memory. Reflection and abstraction are the neuronal processes that move sensory experience into long-term memory and integrate it into existing neuronal networks. Most of the classes that I took provided little to no time for reflection and abstraction or for feedback of tests; thus, I have forgotten most of what I learned. What a sad waste of everybody's efforts and investments. So in my writing classes, I might allow my students to blog about the concepts I'm trying to teach, encouraging them to make the connections between the new stuff and what they already know. Class discussion is also a good tool for this reflection. I might encourage abstraction by having student bring in writing assignments from other classes and plan in groups how they might solve those assignments. We might also allow for feedback from those assignments, assuming there is time.

Then, short, shared writings in class are a good way to test new knowledge and feed the results back into our brains. Actually, I don't see why this wouldn't work in most any class. Maybe I'm just biased, but I think that writing is one of the best tools we have for integrating new information into our existing neuronal networks, thereby turning it into knowledge.
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