Welcome to the QUINT Observation Systems Seminars (OBS seminars). This series will discuss classroom observation systems as a tool for understanding and improving teaching quality.
This and upcoming OBS seminars are open for all interested parties. We want it to become a meeting arena for scholars genuinely interested in observation systems and related issues. Therefore we recommend that you join our network by subscribing to the network mailing list. You must confirm your email address in the confirmation email you receive to complete signing up to the mailing list.
If you are interested in presenting your research or have questions, please contact the organizer, QUINT Postdoctoral Fellow Mark White.
In this seminar, taking place on December 7th at 10am ET/ 16.00 CET, Jing Liu will be presenting work around a fully automated approach to measure teacher uptake of student ideas. The title of the talk is Measuring Teachers’ Uptake of Student Ideas.
Abstract: Teachers’ uptake of student ideas promotes dialogic instruction by amplifying student voices and giving them agency in the learning process, unlike monologic instruction where teachers lecture at students. Despite extensive research showing the positive impact of uptake on student learning and achievement, measuring and improving teachers’ uptake at scale is challenging as existing methods require manual annotation by experts and are prohibitively resource-intensive. We propose a fully automated approach to measure teachers’ uptake and examine its validity across a range of educational settings. We then conduct two experiments to evaluate whether providing teachers feedback on their performance in uptake improves their subsequent teaching performance.
Bio: Jing Liu is an Assistant Professor in Education Policy at the University of Maryland College Park. 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. Before he joined UMD, he spent two years as a Postdoctoral Research Associate at Brown University’s Annenberg Institute. Dr. Liu's research uses rigorous quantitative evidence to evaluate and inform education policies at the national, state, and local levels, with the goal of improving learning opportunities for historically marginalized students in urban areas. His work broadly engages with critical policy issues including student absenteeism, exclusionary discipline, educator’s labor market, school reform, and higher education, with a special interest in the intersection of data science and education policy. In his most recent work, he is working with a group of scholars from computer science, linguistics, and curriculum and instruction, to create an automated system that can provide teachers immediate feedback on their teaching practices using classroom transcript data and natural language processing techniques. His work has appeared in peer-reviewed journals such as the Journal of Public Economics, Journal of Human Resources, Journal of Policy Analysis and Management, and Educational Evaluation and Policy Analysis. Jing Liu's Website