I am sitting in on a MOOC (massive online open course–seriously cool!) about connectivism. The term itself caught my eye, though I had to spend some time disambiguating it with “connectionist” (computational neural net) stuff. I now see it as extending learning theories that spanned behaviorist to constructivism, now connectivism. See this document for the chart, and this page for the first week’s activities.
Actually, I’m fighting a battle with cognitive dissonance here. I’m wrestling with the notion that the chart linked above appears to be trying to answer the wrong question. In my view, the issue is not about how connectivism is different from the other learning theories; rather, the question should be closer to how connectivism builds on earlier theories, since I believe that’s what it is trying to do.
My view is built upon notions of relational biology due to Rashevsky and Rosen. In “Topology and life”*, Rashevsky, one of the founders of mathematical biology, realized that we can tease apart a living organism and count the components (which has the nasty side effect of killing it), but we cannot put it back together. Something in our knowledge of living (complex) things is missing. He set out to find out what that something is. Rosen came along and later and offered a mathematical model called the Metabolism-Repair System which serves as a candidate answer.
A central tenet in the Rashevsky-Rosen concept is that the action lies in the connections.
Rosen authored the book Anticipatory Systems (soon to be reprinted) which essentially (my view) offers the underlying basis for all learning theories: living entities are anticipatory by nature. From the lowest single cell creature to the largest living creatures on Earth, anticipatory behavior is in play, whether it’s simple chemical reactions to sunlight bending a plant to face the sun, or to complex neurological processes from neural firings to brains at work. That model fits all the learning processes, be they conditioned-response or people in networks.
Ok, I just offered a highly reductionist explanation to a massively complex set of processes. I don’t see that as any different from someone saying that process X is better than process Y. At some point, as Rosen was fond of saying in his books, you have to keep asking “why”. I believe that anticipation serves as a foundation on which “why” questions can best be understood and, perhaps, debated. My own answer to cognitive dissonance is to ignore the “my process is better than” arguments and spend time seeing how they each work together.
* Topology and life: In search of general mathematical principles in biology and sociology. Bulletin of Mathematical Biophysics 16 (1954): 317–348