The more I age, the more I think about aging. And with age I am changing my view of health. When I was younger, I thought about health in terms of how good I looked and how much I could lift. Now I am thinking more about health in terms of how well I can…
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This is why ‘Digital Transformation’ is so difficult to define
One of the biggest problems with ‘digital transformation’ is that everyone use sthe term differently. At times, ‘digital transformation’ is used to describe a set of social conditions. At other times, it refers to something we have to do. Still at others (and more commonly) it is something we must consume. Simon Chan has lamented…
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Nobody ACTUALLY needs ‘Digital Transformation’
I’m really interested in how ideas become things with the power to shape reality. My interest is not idle. It’s also not strictly academic (despite the fact that I have written a book on the subject). It comes from a desire to explode hype cycles by working with businesses to understand and address real issues…
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AI, Higher Education, and Standardizing Values in Human Decision-Making
Our current use of AI in higher education involves automating parts (and at times the whole) of the human decision-making process. Where there is automation there is standardization. Where there are decisions, there are values. As a consequence, we can think of one of the functions of AI as the standardization of values. Depending on…
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It’s time to get over the accuracy of predictive models in higher education
How should we approach the evaluation of predictive models in higher education? It is easy to fall into the trap of thinking that the goal of a predictive algorithm is to be as accurate as possible. But, as I have explained previously, the desire to increase the accuracy of a model for its own sake…
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Predictive analytics are not social science: A common misunderstanding with major consequences for higher education
This is the second in my series on common misunderstandings about predictive analytics that hinder their adoption in higher education. Last week I talked about the language of predictive analytics. This week, I want to comment on another common misconception: that predictive analytics (and educational data mining more generally) is a social science.
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What are predictive analytics? And why does the idea create so much confusion?
The greatest barrier to the widespread impact of predictive analytics in higher education is adoption. No matter how great the technology is, if people don’t use it effectively, any potential value is lost. In the early stages of predictive analytics implementations at colleges and universities, a common obstacle comes in the form of questions that…
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