Learning with Big Data: The Future of Education

Viktor Mayer-Schönberger & Kenneth Cukier

60 pages, Houghton Mifflin Harcourt, 2014

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Luis von Ahn looks like your typical American college student, and acts like one too. He likes to play video games. He speeds around in a blue sports car. And like a modern-day Tom Sawyer, he likes to get others to do his work for him. But looks are deceiving. In fact, von Ahn is one of the world’s most distinguished computer science professors. And he’s put about a billion people to work.



But there is a third benefit: all the “data exhaust” that Duolingo collects as a byproduct of people interacting with the site—information like how long it takes someone to become proficient in a certain aspect of a language, how much practice is optimal, the consequences of missing a few days, and so on. All this data, von Ahn realized, could be processed in a way that let him see how people learn best. It’s something we aren’t very easily able to do in a nondigital setting. But considering that in 2013 Duolingo had around one million visitors a day, who spent more than 30 minutes each on the site, he had a huge population to study.

The most important insight von Ahn has uncovered is that the very question “how people learn best” is wrong. It’s not about how “people” learn best—but which people, specifically. There has been little empirical work on what is the best way to teach a foreign language, he explains. There are lots of theories, positing that, say, one should teach adjectives before adverbs. But there is little hard data. And even when data exists, von Ahn notes, it’s usually at such a small scale—a study of a few hundred students, for example—that using it to reach a generalizable finding is shaky at best. Why not base a conclusion on tens of millions of students over many years? With Duolingo, this is now becoming possible.

Crunching Duolingo’s data, von Ahn spotted a significant finding. The best way to teach a language differs, depending on the students’ native tongue and the language they’re trying to acquire. In the case of Spanish speakers learning English, it’s common to teach pronouns early on: words like “he,” “she,” and “it.” But he found that the term “it” tends to confuse and create anxiety for Spanish speakers, since the word doesn’t easily translate into their language. So von Ahn ran a few tests. Teaching “he” and “she” but delaying the introduction of “it” until a few weeks later dramatically improves the number of people who stick with learning English rather than drop out.

Some of his findings are counterintuitive: women do better at sports terms; men lead them in cooking- and food-related words. In Italy, women as a group learn English better than men. And more such insights are popping up all the time.

The story of Duolingo underscores one of the most promising ways that big data is reshaping education. It is a lens into three core qualities that will improve learning: feedback, individualization, and probabilistic predictions.

Excerpted from Learning with Big Data: The Future of Education by Viktor Mayer-Schönberger and Kenneth Cukier (Houghton Mifflin Harcourt, 2014)

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