Human Development Colloquium: Jing Liu (TLPL)

Maryland Language Science Center | Human Development and Quantitative Methodology Wednesday, February 7, 2024 12:30 pm - 2:00 pm Benjamin Building, 1107

Using Natural Language Processing to Measure and Improve Teaching Effectiveness: Evidence from Randomized Controlled Trials Classroom observations are central to education evaluation and improvement efforts but face challenges of cost, scalability, and measurement error. Advances in natural language processing (NLP) techniques provide a potentially transformative approach for instructional measurement and feedback. This talk will introduce M-Powering Teachers (MPT), an NLP system that measures high-leverage teaching practices and delivers automated feedback to improve instructional quality and student outcomes. Through several randomized controlled trials, MPT has demonstrated its promise in virtual and in-person classrooms. Major technical and practical challenges remain for achieving MPT's full potential. Jing Liu is an Assistant Professor in Education Policy at the University of Maryland, College Park, and a research affiliate of the IZA Institute of Labor Economics. Named as a National Academy of Education Sciences/Spencer Dissertation Fellow, he earned his Ph.D. in Economics of Education from Stanford University in 2018. His recent research focuses on two areas: i) leveraging natural language processing to measure and enhance teaching effectiveness across educational contexts; ii) identifying and assessing policies to best prepare students for an AI-driven future. Dr. Liu’s research has appeared in leading peer-reviewed outlets across disciplines, including economics, education, public policy, and computer science.

Add to Calendar 02/07/24 12:30:00 02/07/24 14:00:00 America/New_York Human Development Colloquium: Jing Liu (TLPL) Using Natural Language Processing to Measure and Improve Teaching Effectiveness: Evidence from Randomized Controlled Trials Classroom observations are central to education evaluation and improvement efforts but face challenges of cost, scalability, and measurement error. Advances in natural language processing (NLP) techniques provide a potentially transformative approach for instructional measurement and feedback. This talk will introduce M-Powering Teachers (MPT), an NLP system that measures high-leverage teaching practices and delivers automated feedback to improve instructional quality and student outcomes. Through several randomized controlled trials, MPT has demonstrated its promise in virtual and in-person classrooms. Major technical and practical challenges remain for achieving MPT's full potential. Jing Liu is an Assistant Professor in Education Policy at the University of Maryland, College Park, and a research affiliate of the IZA Institute of Labor Economics. Named as a National Academy of Education Sciences/Spencer Dissertation Fellow, he earned his Ph.D. in Economics of Education from Stanford University in 2018. His recent research focuses on two areas: i) leveraging natural language processing to measure and enhance teaching effectiveness across educational contexts; ii) identifying and assessing policies to best prepare students for an AI-driven future. Dr. Liu’s research has appeared in leading peer-reviewed outlets across disciplines, including economics, education, public policy, and computer science. Benjamin Building false