Jing Liu Awarded NAEd/Spencer Dissertation Fellowship

May 25, 2017

Congratulations to Jing Liu for receiving 2017 National Academy of Education/Spencer Dissertation Fellowship.

Jing Liu is a Ph.D. candidate in Economics of Education at the Stanford Graduate School of Education. He earned his B.A. in Economics in 2011 and M.A. in Economics of Education in 2013, both from Peking University, China. He also earned a M.A. in Economics in 2016 from Stanford University. His research mainly focuses on using computational social science methods, especially “text as data”, to measure beneficial teacher and peer practices and evaluate their effects in K-16 classrooms. His work has appeared in Journal of Policy Analysis and Management and AERA Open.

Peers, Teachers, and the Mechanism of Education Production—Using High Resolution Data to Understand Education Processes

Decades of research on education production function treats teachers and peers as abstract inputs in education processes. My dissertation intends to extend the understanding of the roles teachers and peers play in education production through using novel empirical approaches to analyze high-resolution data that record the process of education. First, I examine how peer interaction affects student learning outcomes and persistence in online courses offered by a large for-profit university. I measure peer interaction using detailed written communications between students in online discussion board. Second, I measure teachers’ contribution to student engagement in secondary school using students’ unexcused class absences as a proxy. After creating value-added to student attendance, I further investigate how this new dimension of teacher effectiveness influences student high school graduation and dropout above and beyond teachers’ impact on student test scores. Third, I use “text-as-data” methods to create metrics of teacher practices in English Language Arts classrooms at upper elementary school. Different from the conventional approach of teacher observation, I quantify teacher behaviors through their language and interaction with students from word-to-word transcriptions of classroom videos. I then ask what teacher practices make them more effective in terms of contributing to student academic performance.

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