I started teaching Data Science mostly to give kids a strong reason to learn to code. To show them that coding could help them make a difference in the world. I now know that using real datasets and authentic problems is much more important than that. As well as helping to teach critical thinking and problem solving, which are both incredibly valuable, it allows kids to practice being wrong.
Now, that might not sound revolutionary. Unless you went through school getting 100% on every assessment, you probably had plenty of experience of being wrong, right? Well, yes. But being wrong in the way we all have been at school typically comes with a penalty. You lose marks. You fail things. It’s bad. We are encouraged to avoid it. Much better to be right. The ultimate goal is to be perfect every time.
That’s absurdly unrealistic. People aren’t capable of being perfect every time. We lost focus, we get distracted, we make false assumptions, we choose the wrong approach. We make mistakes. It’s human nature. It’s impossible to avoid.
When you penalise wrong answers, you build in a sense of shame and failure to being wrong that most people never get over. It leads to cheating, to covering up of mistakes, and to avoiding doing things where being wrong is a possibility.
How about, instead, we make it the default that you assume that you will be wrong in numerous ways. We make it a fundamental part of the process to figure out those ways, and even reward the finding of those mistakes. In doing so, we give people the freedom to explore, to try new things, and, above all, to learn without fear. We make finding mistakes a positive experience, rather than an admission of failure. We make it possible for people to change their minds, to learn, and to grow.
When you work with real data (assuming you do it properly), you have to learn to build being wrong into your process. You start by figuring out the problems with the data (because real data, like real people, is never perfect). Then you assume there will be problems with your analysis. You assume you’ll be wrong in multiple ways. You go looking for them. You’re happy to find them.
“And, you know, if they say to me, but I mean, you’re a very, very experienced data scientist, isn’t your job to be right the first time around? No, no, it isn’t. If I’m right the first time around, that means I have not learned here anything.” Dr Michael Brand in Make Me Data Literate.
It’s worth listening to the entire episode that I pulled that quote from (or reading the transcript), because we talk a lot about being wrong. Michael says that an astonishingly good success rate is the best indicator that something is wrong. Being a Data Scientist, a Scientist, a good manager, or even a decent human being, requires us not simply to recognise when we have made a mistake, but to go looking for it. To actively check our work, and critically evaluate our own behaviour.
It’s much easier, certainly, to take the first result and run with it blindly, but rather like running with scissors, it has a high risk of not ending well. Similarly, we can assume our behaviour is perfect at all times, and never think about how we could do better, but that does not make for strong relationships, or personal growth.
By having kids solve authentic problems, in all their complex, messy, unpredictable glory, we teach them that life is not perfect, and neither are people. We teach them the value of critically evaluating their own work. We empower them to take pleasure in finding errors in their work, and in telling people about those errors. We make the classroom a richer, more collaborative space, where making mistakes, sharing those mistakes, and using them to learn and grow is rewarded, rather than punished.
Imagine the adults those kids will grow up to be!