It is often interesting to forecast future states. When will self-driving cars become commonplace? When will extra-terrestrial vacations become trendy? When will wi-fi work in every part of my house? These questions and ultimately future outcomes are closely tied to education. In a sense, education is the fundamental building block of future innovation.
In 1967, Seymour Papiert and his team invented the logo programming language. Papiert forecasted a future state where kids would work closely with computers to learn how to learn. He felt that the characteristics of building with technology would ultimately create better problem solvers. While computers are more likely to be used to search for information on Google or to access the Khan Academy application, we are currently moving more towards Papiert’s ultimate vision.
Over the past several years, parents (myself included) have been concerned about the continuous push towards assessment-based learning. Many refer to this as teaching to the test. While, one might see near term improvement when compared to a given group; the only true utility to assessment-based learning is when the answer is already known. From a numerical standpoint, assessment-based learning is easier to measure and reward for both the teacher and the student. However, if we are only solving problems for which the answer is already known, are we really learning?
Computers on the other hand are less deterministic. While we may know what we are trying to solve for, there are not only many paths to a potential solution; there are also many opportunities for failure along the way. One of the key challenges for many who are learning to program is that the computer will do exactly what it is instructed to do. Technology lacks context and the ability for nuanced interpretation. This means that solutions must be very precise even if they initially appear to be inefficient.
The main reason for this disconnect is the technology evolves at a rapid rate. Once programmers learn how to build websites, they must learn how to build mobile applications. Once developers know how to collect payments using Paypal, they must evolve to payment collection using cryptocurrency. Once technologists learn how to allow access using passwords, they must leverage fingerprints for identity.
In essence, the rapidly changing nature of technology provides the ideal proving ground for true learning. By leveraging a constantly changing platform, there is a very low risk that assessment-based learning can be a true measure of understanding. Instead a competence-based model can be used to understand the underlying components – do you understand how to compile and execute a program?, do you understand how to create functions?, or do you understand how to leverage libraries? This competence-based measures provide a more complete understanding of whether or not someone has truly internalized the concept.
In searching for the next big innovation, past performance is not indicative of future results. For example, it wasn’t obvious that Microsoft and Apple would become giant computing companies following the semiconductor work in Silicon Valley. Or, it wasn’t clear that Google would leverage search to become an advertising juggernaut. Or, it wasn’t intuitive that Facebook would leverage nodes and social networking to become a leading technology company. And, it wasn’t clear that an individual or team would leverage cryptography principles to create the 1st digital store of value – Bitcoin in 2009.
Since it is hard to predict the next innovation, it seems like the more wise approach to education is to provide a baseline framework for understanding how to solve problems. Whether it is understanding the true nature of the pain to the development of an elegant solution, building with technology excels in this regard. In addition, one of the most advantageous characteristics of technology-based innovation and development is that there are multiple failure points along the way. From a line of code not functioning as intended to an entire program that works but does not achieve the desired outcome, technology is the ultimate educational sandbox.
Lines of code are like grains of sand in that you can combine them in an infinite number of ways to achieve what your mind has envisioned. This is what Seymour Papiert and his team saw 50 years ago. And this is why the Logo programming language was introduced for kids. It’s going to be awesome to see what the next innovation will be!