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 a few 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.
ABOUT THE SPEAKER
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 as well as all the major artificial intelligence venues such as NIPS, ICML, AAAI, ACL, and EMNLP. His work has been covered by The New York Times, Slate, Forbes, WIRED, and the BBC among others. In past lives, Burr was a postdoc at Carnegie Mellon and earned a PhD from UW-Madison. He currently lives in Pittsburgh, where he gets around by bike and plays guitar in the pop band Delicious Pastries.