by Stephen Downes
Jul 08, 2016
Discussion of some of my recent comments on whether a neural network needs domain knowledge in order to learn. As Matthias Melcher suggests, many artificial neural networks will have the benefit of training by human experts. But what of human neural networks? And how does this play into the idea that learning is sequential? "I think," writes melcher, "while artificial networks need some prerequisite input, human neuronal networks use recognizing from the very beginning and require no indispensable prerequisites." This is enabled by thinking of learning as a process of recognition rather than of representation. "Recognition explains the deeper mechanism of learning as not linear/ sequential (not via fixed isolated representations) but as laminar/ all-at-once (multiple connected features of a pattern)."
I don't really cover blended learning but I found this article quite useful from the perspective of understanding what's happening in the world of education according to Microsoft (Google and Apple also have similar programs). Things like OneDrive, OneNote and Microsoft Forms play a major role (again, see the same thing in Google). It's worth noting that most people working with educational technology are working in this world. Also, I liked this: "An expert is someone who isn't afraid to share how they mess things up while they are learning."
I haven't reported on fMRI research here over the years nog because I magically knew that it was flawed - obviously I didn't - but because I don't trust it. The problem is that the fMRI images are data being interpreted with no way to validate the interpretation. You may as well try to read hard drives by scanning the heat signatures; who is to say your reading is wrong? How bad is the current result? "Some results were so inaccurate, they could be indicating brain activity where there was none." It's the sort of thing that people should have expected. The winner of a igNobel used an fMRI to detect brain activity in a pumpkin and a dead salmon. "The authors note that at the time the poster was presented, between 25-40% of studies on fMRI being published were NOT using the corrected comparisons."
This post reminds me of my own article on change from a few years ago, but it has many more models than I include. I would have preferred to see more discussion of each model, with examples actually included in the text, but the grid format makes for a handy reference. Also, I think it would have been useful to specify that all of these models are at work to one extend or another, varying across contexts and domains. There's a link to a large PDF version. Via Ross Dawson. Related: Six basic emotional arcs of storytelling.
After events such as the changes to Evernote or the shut-down of Google Reader we get constant reminders like this one, that we cannot depend on free. That's true. But crucially, we cannot depend on paid, either. Like when I bought iMovie from Apple and the first update eliminated the timeline view of my movies. After that, all it ever did was generate thumbnails. Or how about those people who bought WebCT and Angel, counting on continued service and support. Or closer to home, my Windows 8 was almost forceably updated to Windows 10, which obsoleted my laptop. I could go on and on about how undependable the stuff we pay for is. So undependibility has nothing to do with whether the software is free. It has everything to do with the business model behind the software, free or otherwise.
A few years ago we invited Clark Aldrich into the Chande 11 online course, where he talked about simulations for learning. Over the years he has created and collected a variety of these under the heading 'short sims' and the slogan 'simple educational simulations work better." He explains that short sims provide a richer experience. They "can present complex processes for students to perform, remembering past decisions." They "can put students in social situations with many possible options." Try one here.
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