Although the data flow isn’t quite there yet for the personalization of stories in the media space the world of business and public data has matured to the point at which it can support both hyper-local and personal reporting. More to the point, I would argue this kind of personalized reporting is the only way in which we will be able to draw out the actionable insights that is contained in that data.
In particular, the automatic generation of personalized reporting with feedback and advice holds the promise of transforming the landscape of education.
But what would this mean?
Let me start with an example. My 14 year old started this year as a freshman in high school and ran into some problems with a Physics test. The moment this happened, my first reaction was straightforward: go talk to your teacher and find out exactly what you did wrong. My point was that it could have been anything: issues with framing the problems, misapplication of formulae, etc. Regardless, in order to figure out what to fix, he needed to know what was broken.
It turned out that he had two critical problems. First, he kept forgetting to include units in his answers. Second, he would make simple computational errors, mostly having to do with flipping the signs in inequalities. Both problems led to wrong or incomplete answers and were easily fixable. But it was crucial for him to understand the problem in order to get to the solution.
Now, he goes to one of the better public schools in Chicago and –luckily– has a teacher who is dedicated to his students. But the reality is that this situation is rare and as a result, this level of personal attention, communication and counsel is often impossible to get to. In short, a one-on-one conversation doesn’t scale.
This problem is amplified as more and more of our educational practices move online. It is hard today for all students to get one-on-one attention in the classroom. As students move online, it is simply impossible.
Narrative Science has begun work to solve this problem. We are starting with data generated by students who are taking online courses aimed at helping them with standardized tests. In particular, we have an initial configuration of our Quill™ technology platform that is able to generate personalized reports to students who take practice tests each month that are not only retrospective, but actually provide specific advice as to actions each of them can take to improve their performance.
Which is to say, each student can receive a report after each practice test that goes beyond the expected feedback:
Testing and practice are part of the learning process. By taking the data that is part of tests as they stand (level of difficultly of the questions, material being tested, types of conceptual errors that the wrong answers indicate) and using the results as a driver for focused communication, we can change the shape of educational progress. By having analysis of that data be part of the process and having focused, personalized reports as the output, we can achieve scale and improve student performance by improving the feedback and the advice they need.
With the computer, we can create personalized communication and advice at scale. In doing so, we can amplify and transform the educational experience in this country and the world.
