Picture the stock market. Chances are you’re imagining a bunch of people staring frantically at numbers. There are lots of numbers, so many that they need giant screens to display them all. All of the numbers are constantly changing, and each change feeds an already-intense flurry of activity. People are morphing strategies on the fly. In the next room, banks of computers are humming away trying to uncover new and interesting relationships between all of those numbers. The system isn’t perfect, but it’s pretty good at allocating resources to grow the economy, good enough that it’s failures rather than its successes are front page news.
Now picture the market for innovative ideas in education. This market is no less important, but it looks very different. There’s far, far less data. The data can’t be aggregated on a bunch of screens, it’s siloed in statistics and reports scattered across the country. That siloed data is updated rarely, usually just a few times a year. Sometimes these updates set off flurries of activity, but just as often people fail to see their significance. Individual researchers look at trends, but the data is so fragmented that intensive data mining is severely limited. This system makes headlines when it succeeds.
I’m not arguing that our education system should look or act like Wall Street, I’m saying that the way we collect, organize and analyze data about education has a powerful impact on the way that we allocate resources to improve the education system. Data on education is expensive to acquire, more expensive to verify, and tricky to categorize. As long as that’s the case, the way that the education system innovates will look less like Google and more like the Vatican.
Well guess what, that’s changing.
There is a tsunami of data speeding for the education system’s coastline, and it was triggered by an earthquake called the smartphone. Let’s jump ahead seven years to the point when smart phones are as ubiquitous in low-income classrooms as the number two pencil. Consider the explosion of smartphones in low-income markets around the world, their rapid commodification, and the fact that a rapidly growing percentage of mobile phones are being donated to charity. It’s only a matter of time before enough schools will have enough spare iPhone-level technology available to lend phones to the kids who don’t have them.
Right now kids interact with teachers and each other by talking, raising their hands, and writing on paper. The only real visibility we get into classrooms is when kids write on paper that is standardized and machine-readable, a data collection process that has profoundly transformed the face of public education. Now consider the crappy, out-of-date cell phone of 2018. It can convert human speech to text. It can read body movements like the recently released Microsoft Kinect. You want data? Get ready to track eye movements.
This coming tsunami raises opportunities and challenges for educators, researchers, parents and kids. Assuming that the data can be anonymized, validated and organized, this trend also opens the door for profound revolutions in educational funding. Innovators can be rewarded for creating learning concept by concept, and enough data will exists to uncover complex relationships between things like abstract logic and nutrition. Funders will have real-time dashboards telling them what kids are learning, what they’re failing to learn, what they’re choosing to learn, and what factors outside of the classroom are impacting their performance. They can be on the phone like Wall Street brokers, directing funding minute by minute to the places where it can have the most impact.
This kind of extreme transparency raises larger moral questions that our society must grapple with, but there is little doubt that it is coming and leaders in educational innovation need to be prepared. That tsunami of data will be an unintelligible mess without a taxonomy to make sense of it all. Self-generating taxonomies like XML or the Dewey Decimal System are the only way that the breadth of educational data will ever be categorizable. Funders should start rewarding innovators who can provide data which fits these taxonomies as a way to begin replacing standardizes testing with something much, much more powerful. Above all, we should see technology in the classroom as a tool which transforms decision-makers even more than it transforms kids.