CS Colloquium: Burr Settles (Duolingo)
Duolingo: Improving Language Learning and Assessment with Data
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, developing statistical machine learning systems to improve learning, engagement, and assessment. He also runs FAWM.ORG (a global collaborative songwriting experiment) and is the author of Active Learning—a text on AI algorithms that are curious and exploratory (if you will). His research has been published in numerous journals and conferences, and has been featured in The New York Times, Slate, Forbes, and WIRED. In past lives, he was a postdoc at Carnegie Mellon and earned his PhD from UW-Madison. He lives in Pittsburgh, where he gets around by bike and plays guitar in the pop band Delicious Pastries.
Friday, January 26 at 11:00am