Connectivism, Neuroscience, and Education

I have never been comfortable with proclamations by educators or scientists (and yes, there is a difference) about how the brain works. The logical fallacy goes something like this: "we have isolated a mechanism in the brain, learning takes place in the brain; therefore, we now know how learning works." Whenever a psychologist says something smug like "the brain doesn't work that way" (around 1:21), I want to pull my hair out. The latest theories about how the brain supposedly works also include huge gaps in our understanding of how the brain supposedly works and plenty of lines of research that may one day soon give us a more complete picture of how the brain supposedly works. The idea is that if we know how the brain is supposed to work, then we will somehow know how we learn. There are so many layers here though that it seems to be an impossible task. First, it assumes a purely mechanistic view of the mind and learning. Not that we have to get metaphysical, but this could be something that is so complicated that thinking of the mind as a flow chart or a network may not even scratch the surface of what is really happening. When educators talk about what neuroscience has to say about learning, we have to remember that neuroscientists aren't even sure what neuroscience has to say about neuroscience. It is a difficult field because each year brings in a new raft of technologies that reveals more and more about the physical properties, chemical reactions, and neural connections in the brain. But I think there is some promising work in neuroscience that we should be keeping an eye on as educators. One of the more interesting lines of research includes the mathematical models around "deep learning." I think this is finally getting at the complexity necessary to account for the complexity of thinking, language, and learning.

I think there are some promising avenues of discovery in the work of Gary Marcus that could one day help address how we learn. Gary Marcus describes deep learning this way: "Instead of linear logic, deep learning is based on theories of how the human brain works. The program is made of tangled layers of interconnected nodes. It learns by rearranging connections between nodes after each new experience." In other words, the brain is not seen as a series of connected flowcharts but as intersecting nets of connections that create patterns.

Additionally, Geoffrey Hinton describes the brain as a holograph. Daniela Hernandez writes about Hinton in Wired saying that "Hinton was fascinated by the idea that the brain stores memories in much the same way. Rather than keeping them in a single location, it spreads them across its enormous network of neurons."What I like about Hinton is that he says that his work involves creating computer models of intelligence and he seems to avoid the heavy handed proclamations of discovering how learning works. His work discusses "machine learning" which is an entirely different concept. I think it is very important to remember that we are talking about models and not "how the brain works." The networks involved in learning are even more complex than his model because our layers include language, behavior, culture, society, etc. Never mind the chemical and quantum connections in the brain. It is just possible that one day Hinton's work can speak to the complexity of the interplay of all of those networks and their seemingly infinite interrelations.

How does this shape my practice as an educator? I teach workshops on concept mapping and have used concept mapping in my classes, not because I feel that they somehow mimic the way the brain learns but because it is an engaging learning and teaching method that provides opportunities to utilize visual and kinesthetic learning modalities as well as using critical analysis. In other words, it is a method of teaching and learning that engages multiple ways of knowing. And it may also be a good metaphor for how learning may occour in networks, including neural networks. I have seen this discussion around the learning theory, Connectivism. I think we could go into any learning theory and use it, somewhat clumsily, as a way to discuss how learning arises out of the formation and interplay of network, but fortunately George Seimens and Stephen Downes have done a better job with their work around Connectivism.

Related articles

• Theories of Learning. Connectivism: A new type of learning for the digital age (sequenturverbe.wordpress.com)
• Technology Can Read Your Mind, Privacy Advocates Concerned? (securestate.com)
• Theories Related to Connectivism (downes.ca)
• Meet the Man Google Hired to Make AI a Reality (wired.com)

Notes from Educause: Ramachandran

I am interested in Ramachandran and neurology lately because of claims made by George Siemens and others that there is a direct correlation between how the brain physically functions and how we learn (neurons connecting = learning). I think that there is a lot of psychology that happens between someone firing a neuron and writing an email. There are things that are happening in the brain (including neurons firing) but things like memories are distributed through out the brain. George sent out an interesting article on this called "Memories Are Made of This."

There are 100 billion neurons in the brain and there are ten thousand connections per neuron. The different possible states based on these connections out number the elementary particles in the entire universe. Despite that complexity, we can isolate particular parts of the brain by function based on what we learn from people who have had accidents that slightly damage the brain (strokes or accidents).

Different areas of the brain have different functions but that is different than making a judgement about what is happening at the level of the neuron.

Ramachandran studied the phantom limb syndrome. This happens because the brain maps out the nervous system in a particular part of the brain. The brain remaps the hand into the another portion of the brain. A physical event like amputation doesn't just change the body but rewires the brain. Learned paralysis carries over into the phantom limb. He had shown the connection between visual feedback and relieving pain and paralysis in phantom limbs. Used a mirror in his experiments - having patients move their functioning limb in a mirror and the patients are able to remap the state of their phantom limb with the illusion.

There are things called "mirror neurons" "empathy neurons" - they fire when we reach out and grab something but they also fire when we watch someone grab something. There is a malleability of connections. There is someting in the brain that tells us not to feel pain when someone else is poked but that goes away when we have phantom limbs.

He also studied "synesthesia." Theorie about why people see colors is that they are crazy, on drugs, or something that someone has done in childhood, or that they are being metaphorical. He says that it is a concrete phenomena in the brain - one in 50 are synesthetic. Color and number synesthesia is the most common and the areas are right next to one another in the brain. There is increased "white matter" between the areas. It is genetic, why? Our genes are involved in pruning excess connections in the brain in fetal brain development. In higher sysesthetes days and weeks are colored.

Poets, novelists, and artists are good at creating metaphors - making connections - linking concepts in the brain. Artists are 8 times more likely to be synesthetes. Not everyone has this.

The metaphorical allows us to engage in abstraction. Vision, hearing and touch section of the brain are involved in creating metaphors and abstraction.

Are there unique brain structures from other animals? We do have specialized brain structions the are of the brain that engages in cross modal abstraction. He called personality a neural phenomena.

My sense from this presentation is that we are still a long way off from making generalized statements about teaching and learning from brain imaging studies.