Anyone familiar with K-12 education knows the quality of a teacher matters drastically more than anything else in schools — including services, facilities, and even class size. In fact, just three years of highly effective teachers can completely reverse the effects of the achievement gap for a disadvantaged student.
But what makes someone a great teacher?
If you ask ten different people, you’ll get ten different answers. Grit. Organization. Dedication. Classroom management. Cultural sensitivity. This is a question that everyone in education has tried to answer and yet, despite many strongly held opinions, nobody really knows for sure.
Over the years, researchers have examined quantifiable factors we might use to predict someone’s likelihood of becoming an effective teacher. Tens of millions of dollars later, the conclusion has essentially been an anti-climactic “GPA predicts teacher quality… sometimes.”
Living in Silicon Valley, it’s fascinating to me that companies apply advanced data science techniques to some of the most mundane problems of our time — optimizing dating profiles and saving us 5 minutes on our route to a pilates class — and yet, hiring managers in school districts, who make critical, life-changing decisions for millions of students, are using some of the most antiquated tools around to identify and hire talent.
What if there were a massive data set that contained rich information about prospective teachers — things like content knowledge, pedagogical approach, work experience, academic background, and more? Could we leverage this information to predict which of them would turn out to be those life-changing, highly impactful educators?
This 2016 study of data from D.C. Public Schools says yes.
At Nimble, we agree and we’re doing exactly that. We’ve built a modern K-12 hiring platform that matches application data with outcomes, giving schools insights into which prospective teachers will be most impactful in their classrooms. These insights will not only inform the way schools make decisions about which candidates to hire, but can also inform recruitment and professional development across the field.
In a highly-skilled profession like teaching, where talent can make or break a student’s life, the tools we use to inform our choices have to be the best. Advances in data science have helped us improve so many areas of our lives. It’s time we directed this toward one of the most important things we do as a society: determining who is going to teach the next generation.