Wednesday, 9 March 2011

Complex systems and network theory in Connectivism

Hugely frustratingly for me, I was unable to access the Friday night Elluminate discussion due to some computer problems - I managed to listen to some of the discussion through DS106 but only managed to review the recording with all the chat and diagrams today. Over the last few weeks a common thread has been niggling me and so I hope to try in this post to outline what I see as an limitation of viewing learning purely in terms of the connections in a network. The discussion on Friday helped me to identify an antagonism that I think needs to be resolved between network theory and the theory of complex adaptive systems (CAS).

From the readings given on network theory and previous discussions in CCK11 around the idea of feedback, my impression is that Connectivism is heavily based around a specific type or types of network theory (Connectionist, Hebbian, feed-forward - what I'll call Connectivist Network Theory (CNT)) that appears useful for certain problems and has had a lot of success in modelling certain types of behaviours. The debate around "networks" or "systems" on Friday I think highlighted exactly the connection between this type of network theory and the limitations of viewing learning only from the perspective of the internal connections of that network. In my opinion the network theory that underpins Connectivism does not address many of the fundamental implications of a complex entity. And, if a network is not a system or does not need any connection to a system (as argued by Stephen Downes) then why is the theory of CAS relevant to Connectivism?

What I see as the limitation of the currently used network theory is that it does not address the dynamic nature of the entities, agents, humans, etc that are used to model the "nodes" in a network. And let's be clear that these are models  - learning is not "a network" any more than it is "a system" - we use these models because they are useful to a greater or lesser degree. The complexity model for me is a more accurate model because it specifically understands that there is a dynamic relationship not only between the agents in a complex entity but between the complex entity itself and the individual agents. In other words a complex entity is not just the sum of its parts but the product of its parts, its internal and external relationships and interactions. A complex entity learns from these interactions, adapting to circumstances and so embodies its history.

George Siemens suggested that networks can occur within some kind of system, and the example he gave was of a learning network which existed within an educational system, which had been set up by humans with a purpose in mind. I suppose this is probably closer to how I see networks in the sense that networks are the communication channels for some kind of larger scale system. Crucially, the larger scale system is both the emergent result of the connections in the network and also constraining mechanism for the network  (the notion of "enabling constraints" is a key feature of CAS and I have some thoughts about that for another post..) I think that if you extend Stephen's argument that networks don't need a purpose to the human domain then you would have to argue that learning doesn't need any connection to human systems (personal, social, educational, etc) - this argument de-humanizes learning I don't accept it as valid. We humans are ourselves complex entities, embedded in larger complex entities (families, institutions, organisations, societies, etc) and have nested within us further complex entities (biological, neurological down to subatomic processes). Learning takes place in society and therefore learning is subject to the influences of social and economic systems and the complexity of human biological systems.

The Connectivist premise is that it is the connections themselves which are the learning - I wouldn't necessarily disagree with this, however, by making connections, as humans we change (intellectually, emotionally, etc - and there is some recent evidence to suggest that learning causes physical changes in the brain) and as we change our connections change and our abilty to make connections changes. This all takes place within some kind of social system, whether formal education or otherwise - by learning as well as changing ourselves we actually change that social system at some level - as the system is the emergent result of the product of its component parts. In some cases our own "learning" may influence others' learning and through positive feedback loops may cause more obvious changes in the larger system. I personally just don't think that you can model a human network where the human "nodes" are static and just turn on and off depending on the connections to other nodes, human or otherwise - it doesn't take account of the inter-relationship between the whole and the parts. Perhaps I am misunderstanding and that is not how Connectivism sees humans-as-nodes-in-networks but my argument is that this is what CNT is suggesting.

The idea of back-propagation in CNT is modelled around a process whereby a signal is rooted backward from the output in order to adjust the signal more closely with an expected or defined output. This model is based on an assumption that the node is linear and will always react more or less the same way. As stated, humans-as-nodes are inherently non-linear and may react the same way to entrirely different signals or differently to the same signal. In the educational domain, "defined outputs" has a certain similarity to "learning outcomes" - expected responses that we are somehow trying to atune students to acheiving. To use this course as example, in CCK11each person is going to make connections depending on their initial set of connections (learning, understandings, interests). For myself, that means making connections between Connectivism and Complexity Theory - there are no learning outcomes and there is no defined output in CCK11 (are there learning outcomes defined for those who are taking this course for certification?). My understanding is the emergent product of all the interactions I have with my networks  - not just CCK11; this all takes place within my human life, within social and environmental conditions of which I am an interdependent part - so "my networks" involve many nested, interconnected systems.

In summary, my opinion is that CNT is not easily compatible with the theory of complex adaptive systems. That's not to say I don't find Connectivism a potentially useful model - I would just like to hopefully open up some debate around the usefulness of CNT. Also I can't quite grasp the intention of introducing complex adaptive systems but to argue that networks are not systems.

Discrete State Turing Patterns by Jonathan McCabe (used under Creative Commons Licence)

Thursday, 3 March 2011

Complexity and Complex Adaptive Systems - some further references

This is a list of some of the references to Complexity Theory and Complex Adaptive Systems that I have found useful - most are focussed on the these theories as applied to the educational domain. Some of the references here are behind a subscription paywall and some are books but where possible I have given a direct link to the full text if freely available.

Campbell, J., Flynn, J. D., & Hay, J. (2003). The Group Development Process Seen Through the Lens of Complexity Theory.International Scientific Journal of Methods and Models of Complexity, 6, 1-33.(

Carr-Chellman, A. (2000). The New Sciences & Systematic Change in Education. Educational Technology, 40, 28-37

Davis, B. (2008). Complexity and Education: Vital simultaneities. Educational Philosophy and Theory, 40, (

            Davis, B. (2004). Inventions of Teaching: A Genealogy. New York: Lawrence Erlbaum Associates.

            Davis, B. & Sumara, D. (2006). Complexity and education: Inquiries into learning, teaching and research. New York: Lawrence Erlbaum Associates

            Fraser, S. W. & Greenhalgh, T. (2001). Complexity science: Coping with complexity: educating for capability. BMJ, 323, 799-803.

            Haggis, T. (2009). Beyond 'mutual constitution': looking at learning and context from the perspective of complexity theory. In G.J.J.Biesta, R. Edwards, & M. Thorpe (Eds.), Rethinking Contexts for Learning and Teaching: Communities, Activities and Networks (pp. 44-60). London: Routledge (of Taylor & Francis). (

            Doll, W., Jr. (1986). Prigogine: A New Sense of Order, A New Curriculum. Theory into Practice, 25, 10-16.

            Plsek, P. E. & Greenhalgh, T. (2001). Complexity science: The challenge of complexity in health care. BMJ, 323, 625-628. (

            Tosey, P. (2002). Teaching on the edge of chaos. Complexity theory, learning systems and enhancement. (

 Waldrop, M. (1992). Complexity: The Emerging Science at the Edge of Order and Chaos. London: Simon & Schuster.

Thursday, 10 February 2011

Networks and Feedback

Over the last two or three weeks we have had a lot of discussion about networks and how Connectivism is really focused on how learning takes place across these networks. One thing that hasn't really been discussed is the notion of feedback loops in relation to networks and I believe that understanding feedback mechanisms are crucial to understanding how networks develop and evolve. So what is a feedback loop? Feedback loops occur when information or an event in a system makes a change to the system and that information or change is then fed back into the system again allowing the system to respond to the information or event.

There are two types of feedback mechanism: positive and negative. Negative feedback in a dynamic system  is a constraining mechanism, keeping the system within certain boundaries  - it is a limiting function that helps to maintain system equilibrium - think of a thermostat - as the temperature reaches a certain temperature the thermostat cuts the heat source so that the temperature cannot continue to rise, if the temperature falls below a certain level the thermostat again connects the heat source to allow the temperature to rise. In social terms negative feedback can be seen in the use of rules, laws, social norms, etc..Positive feedback reflects and amplifies some aspect of the system and is often exponential in the sense that the amplification can happen very quickly over a relatively short period of time. Most people have probably experienced positive audio feedback, whereby a microphone, too close to speakers creates a situation where the sound signal very quickly gets louder and louder  - the microphone picks up the sound coming out of the speakers which is then fed back through the speakers, picked up by the microphone and so on until painful on the ears! We can see a current example of positive feedback in a social system context in Egypt, where after years of some degree of relative dynamic balance, the pro-democracy movement has been very quickly amplified, threatening the existing system's stability. Positive feedback loops can allow quite dramatic change to occur within a system.

In complex systems, both negative and positive feedback mechanisms are necessary for a dynamic balance of stability and instability. Without positive feedback, negative feedback would prevent any development in the system and if positive feedback went unchecked it could cause the system to collapse. It seems to me that networks are the delivery routes for feedback. We have talked a lot about the connections in networks (nodes, weak / strong links, etc) but not so much about what is being passed through these connections. As well as data, information and ideas networks are the conduits for feedback mechanisms. Thinking in terms of the CCK11 network for example, there are the readings and resources which are available across the Internet for the course each week but the network also includes a high interactive element of discussion, blog posts and tweets. Some ideas get amplified through comments, retweets, etc and become important. The CCK11 network is always dynamic - every time one of the participant's understanding changes the network changes.The CCK11 network is interconnected to multiple other networks so when that network changes the connection to the other networks change - I guess a kind of "network ecology".

There are many kinds of learning - take for example a child learning to walk. The process of learning to walk in obviously not read in a book and has nothing to do with the web or social media - but it is a feedback mechanism, feedback between the child's body and brain (neural networks), an instinctive, iterative process over time amplifying certain movements and restricting others until the delicate balance of walking is achieved. In this example no one sees the feedback itself- only the result of it taking place.

In the Elluminate meeting last Friday, there was some discussion around learning and being connected / disconnected and what that might mean. I had the thought that if you look at networks from the perspective of feedback mechanisms - events that cause change in the nodes - then perhaps the degree of connectivity is connected to the degree of available feedback mechanisms, both positive and negative. On a very basic level, if I sit in my room working on trying to understand something but not sharing my thoughts then any feedback taking place is only within the frame of my own neural connections - I will be connecting and referencing previous knowledge and understanding. I may think I really understand something until I share that understanding with someone else and then something they say might change (or reinforce) that understanding - often because they have made a different set of connections themselves. By externalising my understanding, even on a one to one level I have expanded the potential for feedback and assimilation.

When trying to follow the group discussion going on in the live Elluminate sessions I am constantly trying to adjust my perspective - how does idea X fit into my current understanding and if it doesn't fit then why not? What I realise is that "my current understanding" is the emergent result of all the connections I have previously made - not a static, linear structure built on a pre-determined path but an organic ecosystem of understanding that is always in dynamic balance, subject to forces of positive and negative feedback.


Thursday, 3 February 2011

The Nature vs Nurture debate

This morning there was an interesting article on the BBC website about research into how much our genes influence our learning ( . The research suggests that our genetic disposition does play a very important role in the learning process, strengthening the case for "personalised learning".

I had been thinking about this question a couple of weeks ago after quite a profound experience. It was my niece's 21st Birthday party and at one point I saw my sister writing something down. I noticed that she was holding her pen in an unusual way - the same way that I hold my pen, with my thumb and two fingers above, resting on my 3rd and 4th fingers. I have always held pens and pencils like this and thought that I had just "learnt wrong" when I was a small child. Everyone else seemed to hold theirs with their 1st finger above, resting on their other 3 fingers. I was fascinated - I asked my dad to write something and amazingly he held his pen the same way, and then I asked my nephew...again the same way.

I was really blown away by how something like this could have remained invisible to me my whole life and I immediately started thinking of the implications for learning. Genetic traits might lead to seemingly obvious examples such as the way someone holds a pen (but even I didn't notice this) but what about psychological and other less physical examples? My dad, sister and nephew are all quite different from me externally but how do I know if there are key genetic traits that run through the way that we all have gone about learning  - what else might we share? Are there other things that I think I may have learned "wrong" that are actually quirks of my genes and so I am predisposed to following a particular learning pathway?

I'm not sure how this relates to the #CCK11 course but for me it is something that has to influence learning therefore there must be a connection - I just need to find it. My initial thoughts are (purely specutively I stress!) that from a Connectivist perspective certain genetic traits may be realised in our brains through a favouring of certain neural pathways - and these become early nodes in our emerging learning network.

If anyone has any thoughts on the matter that would be great. Also if anyone has had similar experiences or knows of any neuroscientific research in this area I would be very interested to know.


"DNA Molecule Display" used under CC licence by net_efekt

Friday, 28 January 2011

Frames, metaphors and neuroplasticity

A couple of weeks ago I stumbled across a YouTube video ( of neuroscientist Richard Davidson talking about neuroplasticity and the research showing that social-emotional learning makes physical changes in the brain. Interventions to change people's behaviour actually change the neural circuitry in the brain - changing brain function and brain structure.

Yesterday I watched the video(short version) of George Lakoff ( talking about the work of Goffman and Fillmore - Frames and Frame Analysis. He describes how "frames" are created by social-emotional connections that we interpret as metaphors, and over time these connections become stronger and form specific neural circuits. Our sense-making, and by implication, learning, appears to be directly related to the metaphors and frames we have personally developed over time.

From both the above videos, coming from slightly different perspectives, the evidence appears to show that not only is our learning and understanding constrained and determined by our previous connections and the frames that we have developed, but that new social-emotional connections can lead to new metaphors, new frames and new ways of thinking. Essentially these new connections can physically change our brains and neural circuitry.

Image: "Organic Growth" by jurvetson used from flickr under Creative Commons Licence

Monday, 24 January 2011

Background and initial impressions to my first MOOC

Just prior to Christmas I submitted my MSc dissertation entitled "Complexity Theory and Online Discussion Groups -Emergent Behaviours and Dynamics". The underlying premise of my dissertation rested on the idea that online discussion groups (or any discussion groups for that matter) are potentially complex adaptive systems, and with the same topic, each group can be dynamically very different from another group. Despite interventions by the tutor, some groups just seemed to take off and discuss in depth, while others got by with the bare minimum (or less) of postings. The idea that given the right conditions a discussion group can in certain respects become self-organising leads to some interesting questions, not least of which is "What are the right conditions?" My research was not without its problems, often stemming from the fundamental question of how you can measure interaction and emergent behaviours in a group that you are not part of but are studying outside and in my case in retrospect - the discussions had all taken place the previous Semester. It didn't help that my supervisor didn't know anything at all about complexity theory.

Although Connectivism, as proposed by Georg Siemens and Stephen Downes, was on my radar as something people had been talking about, I was not clear on what it really was all about and what the basis of the theory was. Just before Christmas, George Siemens was talking in Glasgow and I managed to connect to the Elluminate session - I think this is where I first heard about the potential MOOC (Massive Open Online Course) that George and Stephen were going to be running in the New Year. I follow both George and Stephen on Twitter so once the course details were announced  ( I registered and here I am.

This is my first ever blog and so this course has really been the catalyst to start blogging (assuming it goes okay and I continue!) Over the first week I have been engaged with the readings and videos and managed to tune in to some of the second Elluminate session on the Friday. My first impressions of the MOOC format are that I tend to locate resources mainly through Twitter by following the #CCK11 hashtag, as opposed to looking through the blog and discussion postings - however, I think that is just my initial response to the wealth of information and I do want to try to find a way to use the discussion postings directly and links to others' blogs. Even trying to be selective can be quite a time-consuming process.

Just in the first week it has been really interesting to try and see the relationship between Connectivism and Complexity Theory. There are many overlaps but one thing that immediately struck me was how the idea of networks at the neural, conceptual and social levels is very much related to the idea of emergent levels of behaviour and learning at different nested levels - I really want to explore in detail some of the connections to complexity theory in more detail over the next few weeks and we are looking specifically at adaptive systems in Week 7. Of course, working in the Technology Enhanced Learning department, I am very interested in the relationships between technology and learning, generally and specifically.

The things I hope to get out of this MOOC are:
  • An understanding of the relationship between Connectivism and Complexity Theory (as applied to education)
  • A better understanding of Connectivism as a theory and its implications
I hope to do this by:
  • Using the readings
  • Blogging / tweeting regularly
  • Taking part in the Elluminate sessions
  • making  comments on other people's blogs
I wish us all success in expanding our understandings from whatever perspective!