Human Development Colloquium: Jing Liu (TLPL)
Using Natural Language Processing to Measure and Improve Teaching Effectiveness: Evidence from Randomized Controlled Trials
Abstract: 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.
Bio: 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.
