Although this piece first posted at The Conversation was not intended as a response to Daniel Nehring’s request for opinions about effect of ranking-mania on academic labor, Alister Scott’s observations on the current state of British higher education do shine a light on one facet of the larger issues involved.
In The War on Learning, Elizabeth Losh analyses recent trends in post-secondary education and the rhetoric around them. In an effort to identify educational technologies that might actually work, she looks at strategies such as MOOCs, gaming subject matter and remixing pedagogy, writes Susan Marie Martin.
Unintended consequences and little practical improvement could result from England’s plan to give poor students priority in school placement, especially if schools can decide to opt in or out, argue Stephen Gorard and Rebecca Morris.
Gavin Moodie has looked at how printing first challenged then changed–for the better–higher education. Here he suggests more modern forms of technological advancement likely will result in the same.
David Glance argues that the university-shaking predictions once routinely made for massive open online courses have been borne out.
Just as scholarship now is more and more about the generation of economic benefits, for many studying is now less about ‘reading for a degree’ than about ‘getting a degree,’ suggests Daniel Nehring.
Robert Dingwall argues that numeracy and and a grasp of quantitative method of course have a place in the education of a social scientist, but they shouldn’t be the only skills in the graduate’s quiver. How about he ability to walk around, for one?
Some Australians have looked to the United States as a model for revamping Oz’s higher education system. U.S.-based sociologist Steven Ward suggests they ought to take another look.
An article in the journal Psychological Science, “The New Statistics: Why and How” by La Trobe University’s Geoff Cumming, has proved remarkably popular in the years since and is the third-most cited paper published in a Sage journal in 2013. The idea of an autonomous vehicle – i.e., a self-driving car – isn’t particularly new. Leonardo da Vinci had some ideas he […] Social sciences can also inform the design and creation of ethical frameworks and guidelines for AI development and for deployment into systems. Social scientists can contribute expertise: on data quality, equity, and reliability; on how bias manifests in AI algorithms and decision-making processes; on how AI technologies impact marginalized communities and exacerbate existing inequities; and on topics such as fairness, transparency, privacy, and accountability. If schools provide the proper support and resources, they will help educators move from anxiety to empowerment when integrating AI into the classroom. An article in the journal Psychological Science, “The New Statistics: Why and How” by La Trobe University’s Geoff Cumming, has proved remarkably popular in the years since and is the third-most cited paper published in a Sage journal in 2013. By carefully interrogating the system of economic incentives underlying innovations and how technologies are monetized in practice, we can generate a better understanding of the risks, both economic and technological, nurtured by a market’s structure.
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Impact
Paper Opening Science to the New Statistics Proves Its Import a Decade Later
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A Milestone Dataset on the Road to Self-Driving Cars Proves Highly Popular
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Why Social Science? Because It Can Help Contribute to AI That Benefits Society
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Innovation
AI Upskilling Can and Should Empower Business School Faculty
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Paper Opening Science to the New Statistics Proves Its Import a Decade Later
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To Better Forecast AI, We Need to Learn Where Its Money Is Pointing
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