Can big data and predictive analytics help school leaders hire better teachers?
That’s the new pitch being made by two companies: TeacherMatch, from Chicago, and Washington-based Hanover Research. Both claim that their new algorithm-driven teacher-selection tools can predict the impact teacher candidates will have on student test scores should they be hired, which they argue would be a significant upgrade over the screeners currently in use.
Between them, the companies say they have signed up nearly two dozen districts and charter organizations already.
“I’m seeing a shift in the way that [K-12] human resources departments are viewing themselves,” said TeacherMatch CEO Don Fraynd. “There’s a whole new breed of folks who are saying that we’re in charge of finding teachers…shouldn’t we care about student achievement?”
Fraynd and Peter Dodge, the founder and CEO of Hanover Research, which recently unveiled a product called Paragon K-12, both contend that the wealth and accessibility of educational data now available, combined with the advanced statistical modeling that is now widely used in other sectors and industries, have tremendous power to help improve teacher hiring.
“There’s no dearth of data in education, but we rarely put it to work,” Fraynd said.
But not everyone is sold on the concept.
“Overall, this is a good signal of districts getting on board and thinking more systematically about the hiring process,” said Jonah Rockoff, a Columbia University professor who has written extensively about teacher recruitment, hiring, and performance. “But is there a magic formula that can revolutionize teacher hiring? I’ll believe it when I see it.”