After a little reflection, and reading a little more from the forums/blogs etc., I thought I’d ask what Connectivism is trying to be rather than the more obvious ‘What is Connectivism’ – but there is a reason for this, and I think it might help me get to heart of my issues with Connectivism. Apologies for any incoherent rambling below…
First up, the usual caveat: I haven’t completed this weeks required reading. In fact, I can’t access Stephen Downes’ website at the moment (has it crashed?) – and so can’t view the video from last week’s chat between Stephen and George Siemens where they discuss “What is Connectivism” – should be here: http://connect.downes.ca/cgi-bin/page.cgi?post=44447
Here are two things that Connectivism could be:
- A general theory of learning and knowledge in networks. This would not need to claim that all learning occurs in networks – but seek to understand and explain how learning does occur in networks, and use this understanding to develop improved pedagogies/andragogies.
- A grand unified theory of learning in homo-sapiens (incorporating what McLuhan calls ‘the extensions of man’ – i.e. technologies from writing to smartphones and everything in between).
Other people have picked out some quotes from Stephen and George which indicate that they are indeed trying to develop a general theory of learning in networks – e.g.:
A lot of discussions about networks that occur in society fall under the heading of discussions of social networks, so you get people like Duncan Watts and others and you’re looking at stuff like scale-free networks and all of that. And you’re quite right, connectionist literature focuses on what they call neural networks or simulations of neural networks and it’s focused on things like the brain and perception and recognition by computers and so on. Part of my position is that the two phenomena are one and the same, that what we’re seeing at the micro level in the brain is the same kind of thing that we’re seeing in society, that we’re seeing in different ways in different places in society. The same principles that govern crickets interacting with each other govern bloggers citing and quoting each other, govern the development of river systems and trees – those principles are also the principles that govern things like human brains and computer networks set up in certain ways.
This seems quite reasonable in many ways – networks are indeed a distinct class of phenomenon that are open to study irrespective of what the networks are made up of. Which is why, to pick an example I’m familiar with, it is possible to model language change and evolution using mathematical and simulation models that were originally convieved to model physical or biological phenomena. My own experience of this feels like half a life-time ago, but is encapsulated in my PhD thesis, “Computer Models of the Evolution of Language and Languages”, defended way back in September 2003.
The ability to study networks independent of what the networks are of is important in complexity science, and has led to books like ‘Weak Links‘ by Peter Csermely, which has the subtitle: Stabilizers of Complex Systems from Proteins to Social Networks. Proteins, neurons, social networks – all very different, but at some level all the same at a very abstract level.
Where I have problems is (as identified in my last post) is the ‘bit in the middle’ – the conceptual layer of Connectivism. At the bottom layer we have neural networks, at the top we have social and technological networks. In the middle, we learn by developing networks of concepts. As George said in reply to my previous post:
I learned by encountering concepts and connecting them in different ways. In fact, I’d go so far as to state that our understanding is related to how we have connected concepts. An expert has a more nuanced conceptual network – she understands how the introduction of a new element influences what already exists. A psychologist has an easier time learning a new theory of motivation than does a farmer. Why? The existing state of understanding (patterns of connections between concepts/ideas) is more developed in the psychologist in relation to psychological concepts. The farmer, in contrast, will better understand new fertilizers or the impact of weather on particular crops (where to irrigate and when).
And concepts are non-symbolic patterns:
concepts are not words and that’s why it’s not going to be a rule-based system; they are patterns in a network and that like the human brain or a network like society as a whole. In these networks, there’s no specific place where the concept is located. The concept is distributed as a set of connections across the same network and other concepts are embedded in the same network; they form parts of each other and they affect each other. (Stephen Downes, 2007)
It’s here at the level of concepts that Connectivism extends itself from simply trying to describe how learning occurs in networks into a new grand-unified theory of learning – by asking us to adopt this theory in place of the like of Constructivism. Without this middle layer of concepts, Connectivism would perhaps sit more easily alongside a wide range of other learning theories and pedagogies. As it is, trying to explain human learning simply in terms of networks of concepts seems tricky – again in his comments George admits:
The conceptual level then refers to “learning”…and, unfortunately, this is an area that is still rather underdeveloped…
I think that at this middle level, when concepts and networks are invoked we are actually dealing with a metaphor for learning – based on connectionist principles, agreed, but a metaphor nonetheless. And a very vague one at that. Connections are made or are not:
Connections form naturally, through a process of association, and are not ‘constructed’ through some sort of intentional action (Downes 2007)
So how do I use that to develop a pedagogy, or improve my teaching? I model some concepts for students and the connections either form or they don’t? George Siemens’ prepared a What is Connectivism googledoc that emphasises the challenge in developing a practical pedagogy from Connectivism. In a table, a number of learning theories are compared according to a range of factors. From this we see the following:
- Influencing factors in constructivism: Engagement, participation, social, cultural
- Influencing factors in connectivism: Diversity of network, strength of ties, context of occurrence
If I see a student in my class who is playing web-games instead of attempting the current activity I might consider that the student is not engaging with the class and possibly chat with that student and try to develop some strategy to improve that student’s engagement. This clearly is a constructivist approach.
What practical pedagogy does connectivism offer here? The idea that a student might have some specific learning intentions appears to be rejected by connectivism, and the question of engagement does not appear to fit with connectivist theory.
And this is, I think, a symptom of attempting to use general theories about networks and turn them into a grand-unified theory of learning. For me, Connectivism’s biggest issue is scope.
Enough for now. Back to work…
Edit: The table in the GoogleDoc mentioned above has a final row titled “Types of learning best explained” – this implies that Connectivism (at least as seen by George) is not trying to be a grand-unifying learning theory – but one that sits easily alongside others. In which case, why does it need the bit in the middle? After all learning involving “Complex learning, rapid changing core, diverse knowledge sources” could be described without reference to the neural level at all, and without insistence on the networks-of-concepts metaphor.