November 26, 2012
On-line education is using a flawed Creative Commons license
Richard Stallman,
Weblog, November 26, 2012.
When I asked Richard Stallman about the use of open licenses for educational materials, first he complained because I didn't use the word "free", then he said that he wasn't interested in educational content, that his arguments applied specifically to software. Clearly his views have been modified since then, as this post attests. "Educators, and all those who wish to contribute to on-line educational works: please do not to let your work be made non-free," he argues. "Offer your assistance and text to educational works that carry free/libre licenses, preferably copyleft licenses so that all versions of the work must respect teachers' and students' freedom." The problem with this is the Flat World or the OERu scenario - content deposited with the intent that it be available without cost is converted into a commercial product. It's not free if you can't access it. Content is different from software, it can be locked (or 'enclosed') in ways free software cannot, without violating the license.
[Link] [Comment][Tags: Open Educational Resources, Richard Stallman, Online Learning]
Essential Steps on the Way to Learning Something
Gregory Eckrich,
Convergence Training, November 26, 2012.
This is a review of Robert Gagne's 'Nine Events of Instruction', brought to light once again as a result of a LinkedIn discussion. Each of the nine events is discussed briefly, and the author helpfully adds a link in each to an example or article with greater detail. Readers should also consult Gagne's eight phases of learning, which describe 9according to him) what actually happens when people learn. I'm not really a fan of this description and approach, but Gagne's ideas and terminology - like 'chunking', 'selective percption' and 'encoding' - can be found in a lot of educational literature today.
Is “Deep Learning” a Revolution in Artificial Intelligence?
Gary Marcus,
The New Yorker, November 26, 2012.
Critical review of 'deep learning', an approach based in neural networks to emulate human learning in computers. The article is in response to a New York Times article reporting on th work of Geoffrey Hinton, based in Toronto, on new rapid-learning algorithms. The article isn't exactly fair to Hinton's approach, but offers a good overview of the work in neural networks since Frank Rosenblatt's pioneering efforts with the 'perceptron' in the 1950s. If you want to learn more about computer-based neural networks, this video delivered by Hinton at a Google Tech Talk is a good start.
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