Stephen Downes has written a series of counter arguments and ripostes to mine in his half-an-hour blog, split into four distinct but related parts. If you have not already read these and you are seeking a deeper understanding of Downes’s interpretation of connectivism, I think these four relatively brief posts distinguish his position well. After the initial post, which I find compelling and respond to fairly fully below, most of his actual attacks in the later posts are not on my arguments but on a few very specific sentences, and in some cases individual words, taken out of context, that have little to do with what I was arguing about. They do none-the-less provide some very interesting expansions of Downes's ideas and are rich in insights and explanations. I will write more about these other posts at a later date, especially on evolution and networks, but only provide some short, general responses to each of them in this post, mainly to highlight a few substantial inaccuracies and misunderstandings.

 

Downes's first post in this series addresses the central issue that concerns me most:  whether connectivism is better seen as a distinct theory of learning or whether it is better that it represents a family of theories aligned by a number of common themes (such as connectedness, networks, emergence, distributed intelligence, knowledge in non-human entities, etc) and sharing a common purpose (largely making sense of what that entails). This is the heart of the issue for me and Downes states it well.

For me, Downes’s explication of the theory of connectivism and his reasoning behind it is useful and enlightening even if, as I have observed in my previous posts, it is a little flawed/incomplete/confused in places. Like much of Downes’s writing it includes great insights and thought-provoking ideas.  I have no major problems with it if it is presented as one of a number of relevant theories in the family of connectivist ideas. It nudges the conversation forwards, suggests different ways of thinking about the issues, opens up the adjacent possible.  

I have far greater problems accepting Downes's theory as a definitive account of what ‘connectivism' actually refers to ('definitive' as of now - Downes is as clear as anyone that this will of course change over time). But  that is precisely and repeatedly how Downes portrays it.  I realize that, in offering a different account, I am thereby doing something superficially quite similar. The difference is that my intent is to keep the field open, to allow for multiple interpretations and acceptance of alternative perspectives around the central core. Liberal vs conservative connectivism, if you like, or soft vs hard connectivism. I believe that my perspective aligns with what I have seen written of connectivism in many papers that I have read and reviewed, as well as the vision of it that was initially laid out by George Siemens.

As it has emerged in recent years, it looks as though the word 'connectivism' is acquiring a common usage that embraces but extends (and often distorts) the views of Siemens and Downes. In my opinion this is, in process terms, a good thing, though I do recognize that this is arguable and that this is fundamentally what the argument is about. There are gains and losses as we move between different levels of specificity. A more specific and exclusive definition means that we can avoid fuzziness and lack of clarity, using this as a theory that stands or falls on its merits but on which others can be built if it works out well, in a quasi-scientific fashion. This kind of thing is good and I recognize its value. However, a broader, more inclusive definition means that it is easier to straddle boundaries, cross-pollinate ideas, exploit diversity, and find connection and commonality where there might otherwise be ignorance. It also means that the idea can remain useful even though particular details of a given theory within it may be wrong. This does not in any way mean that theories that fall into the category of connectivism cannot be far more precise and focused - quite the contrary. We all very much want good theories that have better descriptive and predictive power, ones that can be proven right or wrong, ones that let us see things we did not see before. Downes’s theory makes perfect sense viewed as one of many that make up the family. In time it will evolve, its strengths will be assimilated, its weaknesses forgotten. This is how many people working in a field can move forwards together. Ideas like connectivism can act as a strong binding mechanism to support diversity in a network without drifting towards a chaotic jumble of random connections  or excessive order that limits dynamic change.

The issues are not dissimilar to those surrounding, for example, the word ‘constructivism’, as it is used in an educational context.  The term and broad family of theories, that originated with Jean Piaget, is used in a great many ways that Piaget may have found inaccurate, such as when applied to earlier thinkers like Vygotsky and Dewey or later ones like Bruner or Maturana, who don’t all agree on even basic constructs like assimilation and accommodation, let alone Piaget's genetic epistemology. But it is a useful shorthand for a broad category of learning theories that makes it easier to see both connections and dissimilarities between them. If it had remained as a label for minor variants on Piaget’s initial theory, it would just be another in the long line of theories that came, were shown to be wrong or incomplete (Piaget had some major methodological issues), and went. Its fuzzy boundaries mean that it remains a powerful sense-making tool, especially for those who are new to the area and in greater need of broad brushstrokes to see the larger patterns. It gives us a metastable point around which diverse ideas can spawn creative solutions. It is a catalyst for change, a way of shifting perspectives away from inappropriate instructivist approaches to more active ones (not that either is inherently a bad thing, but diversity is a very good thing). On the other hand, as Downes rightly noted in his post that started this dialogue, it provides a very fuzzy target whether you are trying to argue for or against it and, perhaps, is nothing more than a convenient label that tells us little about the contents beyond a few assumptions about the active role of the knower in the construction of knowledge.

I am clear where I stand on the issue: diversity and churn is a good thing, but I do accept that this means giving something up. With this in mind, I posted a rough first-try at making sense of my own understanding of connectivism last week, not to offer it as a fixed definition, nor one that I expect many to agree with in every detail, but in the hope that it might be picked apart, demolished and rebuilt by others in different ways.  Though I am not sure what the perimeters of connectivism are, I am persuaded that they are and should remain fuzzy and flexible, and that the boundaries are and should be constantly negotiated and renegotiated.

 

Downes's second post suggests that systems are not networks and vice versa. He has a very different view of the definition of a system than the one that I hold, or the one that people who talk about weather systems, planetary systems, nervous systems and ecosystems hold. My view of systems is that they are concerned with the ways that networked entities (including other systems) interact with and affect one another, and the consequent emergent and/or designed behaviours that we can observe within them.  They are concerned with connected parts that affect one or more other connected parts, be they molecules in a cloud, people in a social network, neurons, planets, stars, blood vessels or networked computers. If that doesn't make them pretty firmly and squarely in the centre of a field concerned with how entities affect other entities in a network then it is hard to see what could. This rather takes the wind out of the sails of Downes’s ensuing arguments that we are not talking about the same things. We are. We may well be using slightly different language and paying attention to slightly different aspects, but this is one territory, not two.

 

Downes's third postdiscusses whether the straw-man evolutionary model of learning that I describe is a fair substitute for Downes's own version of connectivism and, more broadly, whether all self-organizing systems can be described (as I describe them) as learning. Thanks to Downes’s tendency to argue against sentences rather than against arguments, this drifts in some interesting directions. It involves a peculiar but interesting diversion into what death means to humans and an aside about modularity, neither of which have anything whatsoever to do with my arguments, but that I find quite thought-provoking. It does make one point that I think is particularly relevant to our discussion, though. Downes suggests that my model implies purpose and (I think therefore) pre-judgement of the issues, whereas his does not. Neither is an entirely fair characterization. An evolutionary model of the sort I describe does not imply purpose at all (that is one of its strengths) while Downes’s model does not escape the need to see things as systems with boundaries, whether innate or imposed. However, I do agree that the theories I discuss are, very deliberately, intended to apply to systems we have already decided that we care about - specifically those involving people learning with, from and through one another - so in that sense he is quite correct. But then I think that, to be of any value at all beyond the metaphysical, the same is true of Downes’s theory and to suggest otherwise is, though admirably self-consistent with the theory (which is one reason I doubt its ultimate value), a tad optimistic. Downes makes some interesting points about whether species can be seen as learning that demand more attention than I have time for now, suggesting that types do not learn. Perhaps. If we see species as replicating patterns (networks) however, we certainly can see development of those patterns over time that, especially when viewed as part of a much broader network (usually described as an ecosystem), appear to adapt. Whether we describe that adapation as learning is, of course, the point, just as whether we describe the result of all those interactions between entities that Downes describes as learning remains an open question. At the end of his post Downes mentions misappropriations of evolutionary theory that he and I both agree are dangerous and that do not emerge in any way from my account. I’m not sure why he does that.

I will respond to this post at greater length later, because his argument is interesting, he says a lot more and there is a lot more to be said about it than I have briefly summarized here. I will, in particular, make it clearer that an evolutionary model is by definition a networked model and explore a bit more what we might mean by learning, viewed as something done by a network or a system.

 

Downes's fourth postagain argues against a sentence out of context rather than addressing a full argument, going even further down the tunnel by picking out individual words to disagree with, divorced from their context. Downes does provide an excellent account of what is meant by ‘information' that neatly avoids dropping into the obscure technical language of entropy. However, I’m a bit unclear about what relevance it has to my arguments apart from that I suggested a broad interpretation of Downes’s theory that used the word in passing to paint in broad brush strokes what kind of theory it might be. The main point of Downes's post is to compare his view of complex adaptive systems (CASs) and his view of networks. Downes’s account of CASs is very different from that of CAS theorists. He apparently confuses views of closed human systems found in organizations with open complex adaptive systems, and repeats the erroneous claim that all systems, whatever their nature, have a meaning or purpose. This is more or less the polar opposite of what CAS theories tell us and not even entirely a fair characterization of systems theories applied to things like organizations and information systems, let alone ecosystems and weather systems. The whole point of the field of CASs is to describe self-organization in systems of interacting parts - i.e.. networks. It is a field that is concerned with both the emergence of complex system behaviours from simple behaviours, and the emergence of simple behaviours from complex systems (that Cohen pleasingly describes as complicity and simplexity). Downes’s description, "Connectivism is a story about how networks self-organize in a fashion that does not presume any prior conditions such as knowledge about states of affairs in the world or about the meaning and purpose of life and death,” is a pretty fair definition of a CAS. He also makes reference to a banking theory of education which is as far removed from the meaning of what I wrote as it could possibly be, and makes an association between co-adaptation in complex systems and the use of adaptive systems in learning management systems, which is simply wrong. Adaptive systems in LMSs are rule-based computer programs that use learner models, content models and so on to selectively show, hide, or emphasize different things depending on the perceived needs of learners. I do not see what that has to do with co-adaptation in evolutionary systems, unless he is describing adaptive systems such as those I and a few others have written that intentionally aim for self-organization (but those are not found in LMSs, more's the pity!).

Downes ends by stating his confusion about systems theory, asking (as complex adaptive systems theories do) how a system changes its own state and (as complex adaptive systems theories do not) how that change aligns with its own goals. Confusion is a good place to start.

 

This is all I have time for right now but I will return to this as soon as I can make enough time for it, because these ideas are important, worth exploring and materially relevant to how we, as individuals and as a species, can be better learners. I also find these issues endlessly fascinating.