In which I leave it to others to consider the rhizomes
The connection between connective learning (connectivism, if you will) and complexity was, I think, obvious from the start. I seem to recall encountering the Cynefin framework very early on in connectivism explorations– perhaps even as a session in CCK08?
And it became equally obvious that, as one begins to try to explain the whole connective learning thing to others, complexity has to be part of the conversation. So as I catch up on some reading and see that Dave Cormier has mashed up rhizomes, connective learning, and the Cynefin framework, it makes perfect sense to me.
Except, I discovered a while back, apparently it doesn’t make total sense to others. In fact, I used the very same illustration Dave did in a presentation about implications of connectivism shortly after the conclusion of CCK08. And I wound up with the impression that it left the wrong impression. Not because the framework was wrong. Or that the presenter—then or now:-)– was wrong (although perhaps my powers of explanation or lack thereof may have played a role), but simply because the graphic didn’t lend itself as well to a conversation about learning as well as it did to a conversation about problem-solving.
Don’t get me wrong—“real” learning in my mind is ALL about problems and trying to solve them. And as I understand it, considerations of rhizomatic learning are related to but not the same as connectivism, so perhaps relevance is in question. But I offer my experience as an alternative perspective with the thought that maybe it’s a shortcut for anyone who wants to go this direction. The deal with the Cynefin graphic in this permutation is that it seems to get interpreted as bins for sorting things into, kind of like when you go to Ikea in hopes of solving your household storage problems. And the result seemed to be that people walked out with the idea that complex learning concerns are best (or could be) isolated in one corner and addressed with the best practice of not using best practices.
Which, you know, seemed kinda wrong.
Fortunately, by time another presentation opportunity rolled around, I had run into an alternative view, graphically speaking. And I hereby apologize about the sourcing of this, because I think it came from several directions, none for which I can now find appropriate links. The graphic was a nifty chart I saw in a presentation deck from Michael Quinn Patton, whereby I later saw a (subsequentally mentally filed) note somewhere that suggested that it somehow stemmed from Ralph D. Tracey’s conceptualization of complex responsive processes. (The differences and relationships between complex responsive processes and complex adaptive systems are worth consideration, but are well beyond today’s scope, and in part beyond what is essentially very basic knowledge on my part regarding this whole ball of wax. Yes, I’m probably in over my head here.)
In any case, in the interest of throwing out an alternative illustration about learning complexity, here’s the “original” chart as I encountered it:
And here’s my interpretation of learning imposed upon it:
I admit to some trepidation about the somewhat implicitly progressive implications of such a graphic, which, worse case scenario, gets interpreted as a kind of “how wild and crazy are you” challenge. But I have found it works well in exploring people’s comfort zones and even worldviews. Do you sincerely believe that most things can be planned and the future reasonably accounted for? Then here’s a zone of understanding where perhaps only the outside edges are fuzzy. But maybe other folks don’t think that way. So here’s an idea of where they might reside and perhaps a few words describing how they think, and maybe there are some blurry lines you could share. It has been useful for seeing learning as not just about outcomes, categories and choices, but about processes, options, and opportunities.
I think this illustration has helped people understand that learning is not so much about sorting as it is about various continuums. In one example I cite, very specific disciplinary learning that is machine delivered lives down in the lower left hand corner. Much of the rest of life occupies space farther afield. And developing an understanding that, while problems might be usefully categorized, learning can be shifted within and between the simple, complicated, complex, and yes, the chaotic, with some level of personal agency, has been useful. (To be clear, I am not saying that Cynefin understanding doesn’t address this in some form– just that the above graphic seems to work better for me.)
In a recent and connectively related post, George Siemens notes: “It is important to realize that MOOCs are not (yet) an answer to any particular problem. They are an open and ongoing experiment.” This view of MOOCs might explain why there is tension about expectations, responsibilities and assessment in MOOCs, and perhaps this tension can be addressed by understanding that burgeoning MOOCs reside in various locations on the agreement/certainty graphic based on the nature of the learning they offer. (Whether MOOCs are ultimately the right unit of analysis for examining complexity and change in learning is a completely different question that has me somewhat distracted.)