Why it is more important to know "how to learn"

Classical education systems have a problem in our fast-paced 21th century. If you start to study Computer Science today and you finish in 3 years, you have not only learned old stuff, you also had not really the time to upgrade to the new things that come day by day. By the time some college graduate finishes and starts to work, many of the things he learned are likely to be not longer important, while a lot of new ones are all the rage. Especially in CS this is the case. Therefore it is more important to know how to learn and how to adapt on a cross-disciplinary spectrum. And what is more valuable for an employer these days? An employee who can solve highly complicated problems in his special field of work, if given the right input, or someone who can take a new problem, identify and describe the key features of the problem and use that to analyze the problem in a precise and practical fashion.We often hear it: "nowadays we need people who can think outside the box, we need cross-disciplinary educated people, we need up2date people".
I will quote Keith Devlin here, he is a Professor for Mathematics at Stanford University. Even though he is talking about mathematics, you can see this as a general statement.


"This new breed of individuals (well, it's not new, I just don't think anyone has shone a spotlight on
them before) will need to have, above all else, a good conceptual (in an operational sense) understanding of mathematics, its power, its scope, when and how it can be applied, and its limitations. They will also have to have a solid mastery of some basic mathematical skills, but that skills mastery does not have to be stellar. A far more important requirement is that they can work well in teams, often cross-disciplinary teams, they can see things in new ways, they can quickly learn and come up to speed on a new technique that seems to be required, and they are very good at adapting old methods to new situations." from KEITH DEVLIN: Introduction to Mathematical Thinking (Fall 2012) BACKGROUND READING

In layman's terms: rather be MacGyver than Stephen Hawking :)