I’ve been reading George’s writing on the unique ideas in connectivism. Two assertions leap out at me in his list of how connectivism is different from other approaches.
First is the statement that “the same structure of learning that creates neural connections can be found in how we link ideas and in how we connect to people and information sources. One scepter to rule them all.”
This sounds almost exactly like the claim made in John Anderson and Lael Schooler’s 1991 Reflections of the Environment in Memory, which I consider one of the finest pieces of research in our field:
Availability of human memories for specific items shows reliable relationships to frequency, recency, and pattern of prior exposures to the item. These relationships have defied a systematic theoretical treatment. A number of environmental sources (New York Times, parental speech, electronic mail) are examined to show that the probability that a memory will be needed also shows reliable relationships to frequency, recency, and pattern of prior exposures. Moreover, the environmental relationships are the same as the memory relationships. It is argued that human memory has the form it does because it is adapted to these environmental relationships. Models for both the environment and human memory are described. Among the memory phenomena addressed are the practice function, the retention function, the effect of spacing of practice, and the relationship between degree of practice and retention.
Anderson and Schooler provide solid empirical data from multiple domains, strong analysis of that data grounded in their theoretical framework (that the environment is reflected in memory), and mathematical models that accurately embody the relationships they observe. Whether you consider yourself a behaviorist or not, I don’t think a reasonable person can disagree with their conclusions. They’ve simply done too thorough a job clarifying their theoretical framework, gathering relevant raw data from multiple domains, analyzing those data, and arguing their interpretation.
I would be absolutely delighted to see this kind of empirical work done to shore up the nascent theoretical framework called connectivism. I expect that if we put our brains to it for a while we could figure out how to do this. Without any kind of strong empirical grounding, connectivism will be forced to dwell in a “thought experiment” realm with the unfortunate majority of educational research that argues from “obvious” first principles and apparently needs no rigorous validation. This kind of disdain for empirical data is a large part of what is wrong with education more broadly. (Please note that I’m not accusing George of this disdain! I think he would also love to see this kind of work done on connectivism.)
The second statement that leaps out at me is “knowledge is defined as a particular pattern of relationships and learning is defined as the creation of new connections and patterns.”
I understand that it’s hip to downplay the importance of nodes and claim that all the action in a graph is in its edges. However, it’s a very valid epistemological concern to ask questions like “what are the entities that exist in relationship to each other in connectivism?” “What are the entities that, when observed, can be interpreted as existing in patterns?” In other words, “what are the nodes that are connected in connectivism?”
If we’re allowed to talk about extremely impoverished networks in connectivism (I get the sense that many of the people on this train are only interested in very complex / rich networks), let’s consider a very simple graph: a graph with only two nodes and one edge linking them. Set that graph to the side for now, we’ll come back for it in a moment.
Next let’s consider the most basic kind of learning, the kind that you’ve been taught to mock and scorn: memorizing facts. This is sometimes called paired associate learning, because you’re trying to associate two items with each other (a vocabulary word with its definition, a country with its capital, a historical event with its date, etc.) It’s also appropriate to talk about this kind of learning in terms of stimulus-response: you’re supposed to learn that when I say “France” you should say “Paris.” When you reminisce about those good old S-R approaches to learning, you realize that the popular abbreviation S-R almost looks like a very simple graph. A graph with one node labeled S and another labeled R, with a link between them.
Now, I haven’t answered the question “what are the nodes in connectivism” I posed three paragraphs ago. But I believe I just demonstrated that old-fashioned, behaviorist, S-R learning is a simplest case of, and is completely subsumed by, the connectivist framework (as I understand it). Perhaps that’s an interesting enough assertion to hit “publish” and go home for the day on.
