Tony Bates lays it on the line: "There is momentum for change in Canadian institutions but it is too little and too slow in comparison to the changes in the world outside. The race is not against the USA, the U.K. or Australia but against the network platforms that will come in and steal our lunch if we are not more agile and focused. Radical change in the way we teach is needed. Resources need to be reallocated and above all a clear vision is needed of how we should be teaching in a digital age. This is essentially a leadership issue." He's right, of course. But they will never let people with such vision anywhere near the university president's chair.
This is a response to an article by Ellen Kirkpatrick titles The academy I dreamed of for 20 years no longer exists, and I am waking up. "The faculty system, as a whole, is classist and the power dynamics are messed up." Of ourse, it was always thus, but now there's a much greater danger than graduates might end up on the wrong end of this class structure, and it frightens them.
This is a post complaining that people are not attributing OpenUp Resources properly. They require "on each physical page of any printed material, and every format page view of digital material, the attribution statement 'Download for free at openupresources.org.'" David Wiley quite correctly points out that this exceeds the requirement of the CC-by license, and that the CC-by license cannot have extra conditions like this added to the license. Via Jennifer Maddrell, who got it via JR Dingwall.
There's a set of slides in the middle of this presentation (24-30) that really illustrate how graph analysis is used to present data in a way that is equally compelling, and more useful, than a typical narration. Overall, the message of the presentation is that graph analysis is most useful for cases where we have loosely connected data, where we have simplistic and disconnected data models, where the data models can’t keep pace with market dynamic, and where the data models are too abstract. Graphs make these cases concrete and intelligible. A narrative would do the same, but in an entirely arbitrary and often misleading way. The link is to a LinkedIn post, but in case you are blocked, here's a direct link to the slide presentation.
Substitute the word 'educator' for 'leader' and the entire argument is exactly the same. If enjoined to discover what a population actually wants and to then implement it, it seems to me that robots could soon do a better job than our leaders. Not yet, though. The hard part is the first part - determining what people actually want. That's what could be solved with computation, at least in theory. Human leaders fail in the second part: implementing it. Too often, human leaders serve some other agenda, one that acts against the interests of the population.
My general inclination is to support the argument in this post, but two things give me pause. The first is the definition of 'computational thinking', which the author characterizes as follows: "look at the provided information, narrow it down to the most valuable data, find patterns and identify themes." That's not exactly how I would define it, and it seems to me to be a mischaracterization of Jeannette Wing's paper from 2006. And second, while I think that pattern recognition as a skill is important, the suggested deployment seems wrong: giving students lists of states and asking them to guess the basis on which they are sorted. That's not recognition, that's recall.
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