My wife bought me a radar detector recently (what does that tell you?). After about 10 minutes, I realized that what I need is a ticket preventer, not a radar detector. For those designing education reform programs, new models for instruction, or companies like mine that design software, this is a reminder that when making design decisions, be sure you are solving the RIGHT problem.
My radar detector does just what it’s supposed to do. It detects radar, regardless of whether I am at risk of a ticket. It proudly, incessantly chirps its warnings as I plod along in traffic at 8mph. Man, there’s a lot of radar to detect!
Then it hit me. The designers solved the wrong problem brilliantly. My radar detector detects radar, even when there is no chance of me getting a ticket. The user wants to avoid tickets, not to know if there is radar around. The user wants a ticket preventer, not a radar detector.
A ticket preventer wouldn’t go off when I am going 8 mph in a 65 zone. Even better, a ticket preventer would compare my speed to surrounding traffic. If we’re all going 70, my ticket preventer would know it, and would know that a cop isn’t going to single me out, ergo, no chirping.
This list could grow and grow, but you get the point: Problem definition is at the heart of great design (just ask Apple). How does this relate to education? Well, let’s take the comments section of nearly every report card. Every marking period, teachers diligently note whether students are a pleasure to have in class, work well with others, or could do a better job turning their work in on time.
Besides mailing it home with the report card, what do school systems do with this data? Nothing, even though at the same time they will tell you that they have become expert at data collection and data-driven decision-making. Why? Because comments were designed to solve a problem defined as “a way to send a slightly personalized message home”. Nothing wrong with that, but what if we repositioned comments as a qualitative assessment, a type of predicative early warning system whose purpose was to tap into the collective intelligence of the community working with a student, to look for trends, and to signal alerts and praise positive changes.
For instance, what if we asked one question, “how optimistic are you about the future success of this student?” and then looked for patterns over time for that student, and also trends among all the people commenting on that student. Changes in optimism are reflective of a number of factors that individually might not add up to anything given today’s comment model.
Schools are full of opportunities to replace radar detectors with ticket preventers. The process starts with the people responsible for system design taking a hard look at the definition of the problems they are trying to solve. We might find that we’re wildly successful at solving the wrong problem.
That’s what I think. What about you?