Research continues to highlight the psychological as well as economic value of “soft skills” and, slowly, learning organizations and initiatives are starting to pay attention.
Often thought as a subset of social skills, or worse, deemed to be “non-cognitive”, soft skills encompass high-level abilities, most of which are thought unique to humans. Some of them are clear, like written communication, while others are more of a “cloud of skills,” like teamwork, which are hard to “operationalize.” To understand what it takes to teach them and verify their acquisition remains an open question.
The difficulty of defining soft skills can at times make them victims of bias, where teachers, organizations, and employers, give them a lower priority in favor of skills that are easier to measure. Even though the general consensus rightly favors STEM education, in recent years, soft skills have started to become a better predictor of job security and income. But this has not been matched by a focus on soft skills across learning offerings and educational products.
As a result, soft skills continue to be a challenge in terms of data and analytics. As economist David Deming, writing for the NBER Reporter, reminds us, other cognitive skills whose tracking seems so trivial took “decades and millions of dollars systematically improving and refining the measurement.” Only after all that work do we have some idea about the right way to approach tracking previously unquantified units:
It’s okay to begin with “proxies” but never forget what they are. Deming notes how the IQ test was originally developed to “diagnose intellectual delay,” and later on it was discovered to correlate with certain traits. The fact that some use IQ as the be-all-and-all is an overuse of the test’s purpose (at no fault of the test).
Embrace standards at your own peril. Especially when a measurement is in development, it is important to work on an agreed upon language. There are several for learning activity, most notably Caliper, but it can be problematic to promote an already defined standard for an evolving field, especially for large or slow-to-move organizations.
Understand the limitations, and keep refining. This means, of course, utilizing an iterative process that guarantees a feedback loop. But beyond that, it must recognize that what we aim to measure is subject to change as social, economic, and cultural needs evolve.
To be sure, nobody is advocating for stopping STEM education or STEM skills advocacy. But in a global labor market prime for automation at every turn, a proper balance between hard and soft skills could open much welcome avenues for progress. After all, there is a reason why this exact space remains so difficult to code for machines to take over.
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