One of the things I need to reflect on this week in my learning is the sorts of technologies necessary for open education, as opposed to just education per se. I have just read about a series of different technologies that can be considered important including social networks, blogs, embedding and VLE platforms such as moodle.
The last one is of particular interest to me because moodle is an open source software and because it was suggest that the VLEs created using platforms like moodle give learners and educators alike a shared basic skill set to begin with. On this basis another key software came to mind and that is OpenOffice. I say OpenOffice here but what I really mean is any free to use alternative to paid for products such as Microsoft office. Over several years teaching I have regularly come across students who do not have access to Office. These days it is less frequent as universities often have accounts available and there are significant discounts for students once they provide an institutional email address. However, if you are doing a MOOC or dipping in and out of something very informally you are unlikely to have the kind of affiliation to a university that would give you this access. On that basis having software than gives similar functionality and can be saved in interchangeable file types is of great value. There has already been a drive to install this kind of software in place of paid for alternatives in various places. Another technology I am going to suggest (somewhat cheekily as we are only asked for one) is cloud based storage like DropBox or similar. The key here is that this offers free device independent storage which can be shared between individuals so it supports learning almost anywhere but also networks of individuals who can co-construct materials.
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I have just finished watching a video by Dave Cormier on Rhizomatic Learning which I have put in below for reference.
In the video Dave describes what is meant by rhizomatic learning, which builds on the rhizome idea of connections growing in all directions being only constrained by their local surroundings. There was something that instantly appealed to me about this because it reminded me dendrites. Dendrites are the small protrusions that normally extend from the cell body of neurons. They are said to form branch like structures such as that the term given to them collectively is a dendritic tree. Here is what one looks like (taken in around 1901):
But the comparison does not end there - dendrites (and the rest of the cell) are constrained only by the careful balance of chemicals in their surroundings and they are dynamic - connections can be strengthened and weakened or formed and lost altogether over time. When I consider learning to reflect the very process that our brains appear to use to learn (dendritic changes are known to be critical in learning), the idea has a certain beauty to it. Just to reflect a little more on this and see how far I can stretch the brain analogy, let's think about how dendrites do against the principle's laid out:
1. The best teaching prepares people for dealing with uncertainty - the brain loves uncertainty and change. We have clearly evolved to detect change an many ways. This is most clearly illustrated with our sensory systems which adapt to constant stimuli but respond vigorous to changing stimuli. We have found evidence in the brain of calculations involving weightings for bottom-up and top-down information where we cannot be certain. 2. The community can be the curriculum, learning where there is no answer - ok so at brain level we can probably have a debate about what the community would constitute but to me the logical community would be the 86 million neurons and the even larger number of glial cells. And again, of course, most learning takes place when there is no clear answer - as a infant we engage in a number of actions and yet do not necessarily comprehend them - connections in brain develop at a huge rate, only later to be pruned back to what we really need. 3. The rhizome is a a model for learning for uncertainty - dendrites can respond to uncertainty in the most remarkable ways - they are dynamic and responsive. Indeed as infants, when the most connections between dendrites and neighbouring neurons are formed, everything we encounter is filled with uncertainty. 4. Rhizomatic learning works for the complex domain so this is a situation where there is uncertainty and there is a need to experiment and see what happens. As explained above dendrites can form connections of different strengths - these can be up or down regulated or removed altogether - they work well for modelling complex domains. 5. We need to make students responsible for their own learning - ok so this is perhaps where my analogy falls down a little but given the we know teaching about the the plasticity of the brain (the ability to shape and re-shape connections) increases effort beliefs, maybe it does not fall down that much. Neuroscience analogies aside though, I find this approach quite convincing as an educator in theory but I would find it most unnerving as a student and hard to apply in practice as an educator. With my educator hat on:
With my student hat on:
Based on these first thoughts and concerns, I cannot imagine implementing this on a standard programme for undergraduates but I can imagine the approach working for professional development where learners may be spread out, with different experiences and that the learning will flow from those individuals networked together. I could also imagine it working on optional courses designed to support more skills based learning. At the moment I am playing around with ideas on teaching digital citizenship across the university to obtain a formal qualification, assessed by portfolio where students would work together. One of the ideas I was playing with today was looking at the topic of online security and seeing how that could take a variety of different approaches depending on the students involved in the discussions. So in summary, the idea of rhizomatic learning has a certain natural beauty to it and it definitely appeals to the neuroscientist in mean but I would be reluctant to introduce it to learners at the start of their HEI journey, instead seeing this as something extra-curricular or more suited to vocational/professional development contexts. One of the things I am reflecting on this week is connectivism. In particular I have been thinking about whether it is possible to re-version one of my existing modules with a connectivist approach. The module I decided to consider is the 'Academic Teaching Apprenticeship'. I chose this module because it is probably the module I designed with the clearest model of learning in my mind. It draws heavily on ideas of apprenticeship and situation cognition. The module is currently offered as a third year optional module for students on the BSc Psychology Programme at King's College London. It is quite unconventional in terms of how much contact time there is and how it used
Principles that work well in the current module:
Principles that are partially met or could work:
Principles I cannot see how to fully integrate:
The colour wheel of learning: Part of me thinks that the issue here is not connectivism or apprenticeship learning. Indeed a good number of the principles of connectivism could be applied to this apprenticeship module. I think almost any model of learning can have value when employed carefully in an appropriate context but I question the value of mixed models of learning. This reminds me of the colour wheel - some colours complement each others and some oppose each other. I don't image that the apprenticeship model opposes the connectivism but it is perhaps a few steps along the circle. |