In the first installment of this series, we left Professor Mitra at the beginning of his quest to understand why remoteness seems to result in increasingly poorer education outcomes the farther one is from an urban center. His guess was that remote areas didn’t have good enough teachers. This wasn’t any kind of bias against teachers either. His assumption was this: if remote areas have good teachers, they can’t keep them because of poor school infrastructure. And if remote areas have good infrastructure to begin with, they won’t be able to continue the maintenance required to keep it that way and good teachers will leave.

To test his hypothesis, Mitra traveled 300 kilometers away from New Delhi and administered tests to each school he found along the way. He graphed the results and found that more remote a school was, the lower the test results were. But to his surprise, none of his assumptions about infrastructure proved true. Classroom size wasn’t a factor. Poverty levels weren’t a factor. Electricity available wasn’t even a factor!

What proved to be the underlying issue was surprising.

The main culprit—based on the results of questionnaires given to teachers in all the schools along the route—pointed in another direction. His questionnaire was simple. Mitra says he had “one single question for the teachers, which was, would you like to move to an urban, metropolitan area.” The results of this questionnaire were enlightening. As remoteness increased the answer “yes” increased at the same rate that test scores had decreased according to remoteness.

So poor school performance had nothing to do with anything related to infrastructure. It had everything to do with teachers. As Professor Mitra says, “teacher motivation and teacher migration was a powerfully correlated thing with what was happening in primary schools, as opposed to whether the children have enough to eat, and whether they are packed tightly into classrooms and that sort of thing.”

Before being mobbed by a group of angry teachers at this point, I would like to offer up the fact that I spent a lot of time behind an English classroom lectern myself and that this result shouldn’t be perceived by anybody as particularly damning to teachers. What we should take away from this rather surprising result isn’t that teachers are the problem, just that many of our assumptions about how to improve education may be deeply flawed. And if we’ve been making false assumptions about education improvement, divergent education options like home schooling and online education shouldn’t be viewed as less effective education options.

And Professor Mitra’s findings seem to fly in the face of our deeply held assumption that many of our problems stem from lack of financial resources for our more poorly performing public schools.

The solution that Professor Mitra offers is rather simple, but counterintuitive.

He conducted a long series of experiments in self-learning by installing computers in public places in these remote areas. And in Mitra’s second way of measuring remoteness—the urban pockets of poverty that are “socially and economically remote from the rest of the city”—also got the computers. All of these computers were placed in walls at eye-level for young children.

What happened is nothing short of amazing. With no teachers to guide them, children with no previous experience with technology learned how to use the computers proficiently. In all of these experiments small groups of children would form and learned through collaboration with no teacher involved at all. Often, a younger child helped instruct older children. Students with no English at all and no experience with computers, but who had learning CD’s at their disposal, learned enough to inform Professor Mitra upon his return that they needed more processing speed and a better mouse for their computer.

In all of these cases, the level of learning skyrocketed just by introducing a computer and allowing a natural pattern of collaboration to take its course. The question Mitra asks is this. If educational technology programs only raise student learning by 3% in excellent schools, but can boost learning in poor schools by 40%, why are the best schools getting the best education technology?

It’s something we ought to examine seriously as well.

Professor Mitra’s findings would suggest that we’re putting the ed-tech dollars in the wrong schools. The schools who are at the absolute bottom of the academic barrel should be receiving the best and newest ed-tech programs; not the other way around. In the next segment of this series, we’ll explore further examples of education alternatives producing results for Professor Mitra.