I have just finished reading the seminal work of Siemens (2005) on his notion of connectivism, and so I thought I would record some initial thoughts.
A lesson from physics
Firstly, connectivism is painted as a challenge to the traditional learning theories of behaviourism, cognitivism and constructivism. The motivation behind this is the observation that the three dominant theories were formulated before the modern computer age. Thus connectivism seeks to supersede the older theories.
However, it occurred to me that a parallel might be drawn here with scientific theories in physics. In particular, Einstein’s work on general and special relativity improved upon Newtonian physics, but did not completely supplant it. Although it is possible to apply Einsteinian mechanics to a macroscopic dynamics problem, it would be needlessly complicated to do so, whereas modelling the problem using Newtonian dynamics would be far more sensible and still accurate enough for the purposes of a problem of that size.
Perhaps the same is true of connectivism. Maybe on a larger scale of interconnected individuals and organisations, connectivism models more accurately the learning process; however, on an individual level, within the context of a classroom, perhaps learning should be modelled on behaviourist, cognitivist, or constructivist principles.
Mathematics
Another point which occurred to me is the applicability of Siemens’ ideas to mathematics. Siemens talks of rapidly changing fields, where knowledge becomes redundant so quickly that constant learning needs to take place. This doesn’t apply to what I, as a teacher of mathematics, do on a daily basis. I teach concepts and methods which are hundreds (if not thousands) of years old, which are established facts for all time. Pythagoras’ Theorem is eternal: no amount of innovation will change it. Does that mean that the connectivist metaphor has no application to mathematics? Are the knowledge networks fixed, suggesting a cognitivist view would be more appropriate?
Similarly, Siemens plays a high value on the ability of networked computers to automate a lot of what we do, meaning that learning shifts from individuals having to memorise skills and processes to networks increasing their connectedness. Does this also apply to mathematics? Is Siemens suggesting that all calculations should be outsourced to calculators and computer algebra packages, so that learning focuses on more complicated calculations?
If so, this seems to misunderstand how mathematics works. One needs to know how to do the easier calculations in order to be able to do the harder ones. Sure, computers can do calculations in a fraction of a second that learners struggle over for hours, but if learners don’t learn the basics, they won’t be able to program computers to perform more difficult calculations.
What is more, this would miss the point of learning mathematics as learning abstraction. Mathematics is not always about applying calculations to do things; sometimes it is innovating approaches to problems that have no real-world application (yet). If all computation is outsourced to a network, where is the deep individual understanding that is necessary in order to abstract?
Innovation and the role of the teacher
Lastly, I had a thought which seeks to explain why innovation is so important within a connectivist metaphor. If teaching and learning is to some extent outsourced to a network, then this goes some way to explain why there needs to be so much focus on innovation.
As Siemens says, “[t]he pipe is more important than the content within the pipe.” This suggests that the role of the teacher is not to push the material through the pipes, but to find new and/or different ways to extend the network of pipes, and this is at the heart of what innovation is.
However, this leaves the question: if teachers are primarily innovators, who does the actual “teaching” in the traditional sense of the term? Are teachers expected to do both? Or is their job to be outsourced to the network of resources as well?