Summary of Why We Should Train Workers like We Train Machine Learning Algorithms

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Why We Should Train Workers like We Train Machine Learning Algorithms summary
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How do you prepare students for jobs that don’t yet exist? According to Brookings Institution scholar Makada Henry-Nickie, educational systems are putting too much emphasis on specific, teachable skills rather than on preparing students to thrive in a rapidly changing work environment. Taking cues from the way computer scientists program algorithms, Henry-Nickie advocates for an experiential, learning-based curriculum that will help equalize the existing skills gap between advantaged and disadvantaged students. getAbstract recommends her analysis to HR professionals and educational policy makers.

In this summary, you will learn

  • Why hard skills alone won’t prepare students for successful careers and
  • How experiential learning can help disadvantaged students catch up.
 

About the Author

Makada Henry-Nickie is a Fellow of the Brookings Institution as part of its governance studies, race, prosperity and inclusion initiative. 

 

Summary

Educational policies aimed at preparing students for the 21st-century workplace focus too narrowly on skills acquisition. Such a strategy only perpetuates a widening socioeconomic gap between those fortunate enough to acquire the skills that translate into rewarding careers – versus those sadly left behind. Yet skills training alone isn’t enough to succeed in today’s work environment. For one, rapid technological change makes it impossible to predict what specific technical skills will be in demand in the future. Pouring large amounts of public resources into hard-skills training thus may not deliver the expected return on investment. For another, hard-skills training alone doesn’t yet confer important behavioral competencies that are crucial for job success. According to several recent surveys, a majority of employers emphasize problem-solving and teamwork abilities in their candidate selection processes. 


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