From Science Daily:

Human learning is a complex, sometimes mysterious process. Most of us have had experiences where we have struggled to learn something new, but also times when we’ve picked something up nearly effortlessly.

What if a fusion of computer science and psychology could help us understand more about how people learn, making it possible to design ideal lessons?

That long-range goal is moving toward reality thanks to an effort led by professors in the University of Wisconsin-Madison departments of computer sciences, psychology and educational psychology. Their collaborative research aims to break new ground in what computer scientist Jerry Zhu calls “machine teaching”– a twist on the more familiar concept of machine learning.

“My hope is that machine teaching has an impact on the educational world. It’s quite different from how people usually think about education,” says Zhu. “It will give us optimal, personalized lessons for real, human students.”

Machine learning is a well-established subfield of computer science in which experts develop mathematical tools to help computers learn from data and detect patterns. The machine learner (the computer) is like a student. The goal of machine learning is to develop models that will prove useful in the future when dealing with large, often unwieldly data sets. Practical tasks like speech recognition are aided by machine learning.

Machine teaching turns this concept on its ear. Rather than dealing with pools of data and not knowing at the outset what patterns might be revealed through analysis, the researcher in a machine teaching arrangement already…

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