by Stephen Downes
Dec 12, 2014
Have a business idea? Get funded with Coursera
I'll give Coursera the prize here for innovative business strategy as it blends one of its open courses with a series of presentations of new business ideas to venture capitalists, who in turn may fund some of the ideas. I assume (?) Coursera gets some portion of the returns the VCs receive. But as usual, it's students who pay most of the shot, a total of $196 for the course (which Coursera brands as 'three courses'). So the lure here is 'get rich quick' and the payment seems low in comparison to the payoff - except, I wonder just how many of them will ever get near to launching a successful business. This is one of those MOOCs where completion stats do matter.
User Data Manifesto 2.0
The problem with most educational technology is that it fails on at least two of the three items (control over user data access, knowledge of how the data is stored, freedom to choose a platform). "This manifesto aims at defining users’ fundamental rights to their own data in the Internet age. People ought to be free and should not have to pay allegiance to service providers." You can contribute to this draft on the wiki. See also Ben Werdmuller: A trade war is emerging over where you store your data.
Technology In Education: An Integrated Approach
Ruben R. Puentedura,
Ruben R. Puentedura's Weblog,
I liked this presentation because it approaches educational technology a bit differently. First, it maps the SAMR (substitute, augment, modify, replace) into three elements of education (pedagogy, content, technology). Then it maps the observed impact on outcomes of particular technologies to these domains. It then maps all this to the ed tech 'quintet' (social, mobility, visualization, storytelling, gaming). Finally, it analyzes the impact of each on the development of the zone of proximal development (the increased learning we can achieve only with a 'More Knowledgeable Other') and outlines the shape of things to come.
When bad ideas will not die: from classical AI to Linked Data
The opening post is short, but I agree with it pretty much completely, and there's a great discussion that follows that draws out many of the arguments and implications. If it's all new to you, skip down to comment 19, which draws the distinction between classical AI and machine learning AI. In a nutshell, Daniel Lemire is arguing that the new 'Linked Data' approach, which is an heir to the Semantic Web, is an heir to the now discredited 'classical AI' approach to machine intelligence. In the classical approach, you collect all the sentences that describe the world, organize them into subjects and (especially) predicates, and link them together. "Collecting, curating and interpreting billions of predicates is a fundamentally intractable problem. So our AI researchers failed to solve real problems, time and time again."
There Is No Best Programming Language
Computer Science Teacher,
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