In the first part of this extended post, I discussed how I've found biological research patterns to be a useful analogy when doing my work in cognitive education research. I started by trying to characterize the concept of biological evolution, working on a hunch that it was a coordination class concept, which itself was based on a hunch that coordination class concepts can demonstrate the four classical patterns of conceptual change. Although I quickly found that biological evolution fit with many of the criteria for the coordination class model, I got in my own way and started looking at the model pretty closely. I couldn't shake a couple of major thoughts, both of which were rooted in my prior education experiences in neuroscience, developmental biology, and teaching. First off, the coordination class model looks a lot like neural networks, which is a good thing, since - so far - we're making claims about human thinking and learning, and we know from neuroscience that those functions are generated by the brain. Second, I've not yet discovered a conceptual model with an internal and inherent mechanism for learning - that is to say, I kept wondering how it was that a coordination class concept actually changed in learning experiences. Third, I've witnessed a lot of different learning experiences for a number of different high school students first learning about the concept of biological evolution, and I feel totally confident in saying that personal beliefs about the nature of knowledge (fancy: personal epistemology) are part of the conceptual structure and strongly influence the way in which a concept develops - especially the concept of biological evolution. I've also been a part of some incredibly creative learning experiences, and it seemed to me that students with the strongest inventive thinking skills were also the students that formed the most expert-like concepts.
To start answering my first observation that coordination class concept graphs look a lot like neural networks, I did a bit of research into neural networks and learning. Needless to say there is a lot of research on this topic in vivo, and there are also many efforts to model neural networks as abstractions in computer software. One of the interesting ideas that turned up in this process was the idea of scale-free networks. These are, in essence, networks of objects that retain certain properties at different levels of size and complexity. Interestingly, these types of networks exist at (at least) the macromolecular, cellular, and cognitive levels of complexity.
To start answering my second observation that coordination class concepts lacked an internal mechanism for learning, I began researching conceptual change theory literature. I found a simple theory on conceptual change that relied on three progressive processes: plausibility, comprehension, and fruitfulness. Based on my teaching experience, I chose to use these processes as functional categories for different types of parts of thinking in science: plausibility is linked to inventive resources (like imagination), comprehension is linked to sense-making resources (like processing time scales), and fruitfulness is linked to epistemological resources (like knowledge-as-transmitted-stuff). Further literature review has demonstrated that each of these categories can be divided into smaller categories. So, I've started to think of this as the development of a taxonomy for the parts of thinking.
More to come - time to get ready to teach.