In the upcoming “Connectivism & Connective Knowledge” Course, the first week’s topic asks “What is Connectivism?”. I am not so comfortable with a fixed definition. Furthermore, I think its most interesting aspects are not only being a theory of learning, but offering a whole new view for much more. And all of these aspects have in common that they can be illustrated by the neural metaphor.
IMO, a definition or description would be more appropriate for simpler things that do not suffer when they are isolated and formalized. I think, a complex, emerging concept like connectivism is better understood by its relationships. So, rather than “What is…”, I would prefer something like “How is it related”, or connected, to other ideas, or even, to the world.
Connectivism would, IMO, suffer from restricting definitions such as being a learning theory, which has to obey traditional criteria of an empirically provable but very narrow scope of application. Even though the theory is addressing extensive changes and emancipation, this will not increase the perceived scope of what the theory explains but, instead, the prevailing resistance against such changes will further diminish and restrict the conceded scope.
The whole new view, however, that is enabled by connectivism, extends to much more than learning and schools. Downes’ and Siemens’ discussions shed new light on fundamental concepts, such as rules versus patterns, complicated vs. complex, equivalence vs. similarity, and coping with ambiguity and uncertainty. And these consideration render many entrenched practices of the entire knowledge industry questionable.
All these aspects have one thing in common: that they can be illustrated by the neuronal metaphor, the metaphor of a network with nodes and connections, where
“Not all connections are of equal strength in this metaphor” (Wikipedia)
In a neuronal network, a (source) node is not uniquely related to one (target) node, but to multiple other nodes, and not with a probability of 1 but less than 1, so the residual uncertainty can not always be neglected.
- If the nodes each had one single target assigned to them they would form a hierarchy rather than a network.
- If one node would inevitably and with certainty lead to one determinate other node, we could speak of rules rather than patterns.
- If the strengths were equal, we had equivalence rather than similarity.
So-called exact knowledge often consists of a structure of concepts and terms that form a hierarchical tree of “broader terms” and hyponyms or special cases. If an object seems to resist such unique assignment, we add case discriminations. These distinctions add further branches and twigs to the hierarchical tree but never challenge the tree structure. With countless special cases and exceptions, the formation may become arbitrarily complicated, and the imaginative separator line between two concepts may look like a zigzag path, or adopt a folded or pleated shape. This is what the etymological root of complicated is (Latin complicare “to fold together”). The word root nicely visualizes the difference between complicated and complex because the latter comes from Latin plectere “weave, braid, twine”. This cannot be reduced to some hierarchy but always remains a network or a web.So, connectivism and its neuronal connections metaphor, allow to distinguish more clearly between two types of knowledge, one of which is the more adequate one for coping with complexity and uncertainty: connective knowledge.
Two more aspects that can be illustrated by the connection metaphor, apply to my favorite thinking-support tools.
- One is visualization: The spatial proximity of nodes on a map can best cater for relationships that consist only of gradual similarity.
- The other one is bridging the gap between trees and networks by “see also” links: When nodes cannot uniquely be assigned to one distinguished parent node but exhibit only a preponderant affiliation, then the cross-reference is an important means to express the secondary relationships.
All this shows that the connectivist metaphor brings much more profit than just being a theory of how to improve teaching by learning from neuronal networks. Similarly, the Renaissance was not only the singular theory about how to improve, say, architecture, by learning from the antiquity, but instead, it brought about a whole variety of distinct but somehow similar and interrelated new thinking. And I like the Renaissance comparison more than comparing educational change with the reformation and the religious wars.