Student learning data can and should be analyzed to develop new instructional technologies, such as personalized practice schedules and data-driven proficiency assessments. I will describe several projects at Duolingo — the world's most popular language education platform with more than 200 million students worldwide — where we combine vast amounts of learner data with machine learning, computational linguistics, and psychometrics to improve learning, testing, and engagement.
Burr Settles leads the research group at Duolingo, an award-winning website and mobile app offering free language education for the world. He also runs FAWM.ORG, a global annual songwriting experiment. He is the author of Active Learning — an introductory text on machine learning algorithms that are adaptive, curious, and exploratory (if you will). His research has been published in NIPS, ICML, AAAI, ACL, EMNLP, NAACL-HLT, and CHI, and has been covered by The New York Times, Slate, Forbes, WIRED, and the BBC among others. In past lives, he was a postdoc at Carnegie Mellon and earned a PhD from UW-Madison. Burr currently lives in Pittsburgh, where he gets around by bike and plays guitar in the pop band delicious pastries.