Improving Tech Teaching, Human and Machine

Susan Hall
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13 of Today's Hottest Tech Skills

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A New York Times story delves into why so many would-be engineering and science majors bail once they get to college. Hint: David E. Goldberg, an emeritus engineering professor, refers to it as "the math-science death march." Or as the headline puts it: It's just so darn hard.


The article says roughly 40 percent change majors or fail to get any degree. That figure rises to 60 percent if you add in pre-med majors. While students lap up fun hands-on experiments in middle and high school, many find they have no appetite for the dry and difficult material they face in their freshman year that's designed to be the basis for higher-level classes. While some don't have the math background for it, others haven't developed the study skills or the work ethic to slog through.


And though President Obama has called for training 10,000 more engineers a year, Goldberg, who retired from the University of Illinois at Urbana-Champaign, sees the possibility that will happen as "essentially nil." It's an interesting article looking at efforts to improve retention of science and engineering majors at a time when so many employers are crying out for more skilled workers. It features universities such as Notre Dame, which has improved retention in engineering by adding design projects for freshmen to that strong dose of theory and breaking "a deadly lecture" for 400 students into smaller groups.


Meanwhile, a piece at Harvard Business Review makes the case that the real in-demand skill of the future will be in training machines. I've written that teaching business users will be an important skill as companies move to cloud computing. Michael Schrage, an author and research fellow at MIT Sloan School's Center for Digital Business, argues that the real genius of IBM's Watson is its ability to learn and make adjustments accordingly, a trait shared by Apple's tremendously popular Siri personal assistant. He points to this Slate article on machine learning, saying:

Machine intelligence has quickly been overtaken by machine learning as the quantitative discipline redefining cognition, decision, language and psychology. Massively parallel computational architectures have superseded tightly-written dedicated algorithms for solving - or resolving - ambiguously complex problems.

And if machines can learn, someone has to teach them. (Like Patrick McKenzie, whose post "Don't Call Yourself a Programmer, And Other Career Advice" I wrote about Monday, Schrage shares anathema for the word "program.") Writes Schrage:

The human capital implications are compelling: you might be far better off professionally investing time in becoming a better tutor and coach than learning a new computer language. Similarly, if you can be a Cesar Millan of machines - a digital disciplinarian who helps people get more value from their devices much the way the original helps people have healthier relationships with their dogs - you possess a core competence that virtually guarantees a high-impact professional life. With all due respect to Peter Drucker, you can learn a lot from an animal trainer. You need to learn how to help your machines learn if you want to succeed.

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