As scalable learning technologies become more ubiquitous, student data can (and should) be analyzed to develop new instructional technologies, such as personalized practice schedules and data-driven assessments. I will describe several projects at Duolingo -- the world's largest language education platform with more than 200 million students worldwide -- where we combine learner data with machine learning, computational linguistics, and psychometrics to improve learning, testing, and engagement outcomes.
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 -- a text on machine learning algorithms that are adaptive, curious, and exploratory (if you will). His research has been published in Cognitive Science, NeurIPS, 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 lives in Pittsburgh, where he gets around by bike and plays guitar in the pop band delicious pastries.