Jing Liu

Assistant Professor

University of Maryland

United States

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About

Jing Liu is an Assistant Professor at the University of Maryland University, USA. His research areas include automated feedback in educational settings, the impact of noncognitive factors on student success, and racial disparities in disciplinary actions within schools. His recent publications focus on enhancing teacher questioning quality, bridging performance gaps in automatic speech recognition for children, and analyzing disciplinary office referrals among teachers.

Articles (13)

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2025

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2023

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2020

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2020

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2020

Collaborators (2)

Ying Shi

Assistant Professor

Syracuse University

UNITED STATES

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Heather Hill

Hazen-Nicoli Professor in Teacher Learning and Practice

Harvard Graduate School of Education

UNITED STATES

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Jing Liu

Research Interests

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About

Articles (13)

Automated feedback improves teachers’ questioning quality in brick-and-mortar classrooms: Opportunities for further enhancement

Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults

Troublemakers? The Role of Frequent Teacher Referrers in Expanding Racial Disciplinary Disproportionalities

Can Automated Feedback Improve Teachers’ Uptake of Student Ideas? Evidence From a Randomized Controlled Trial in a Large-Scale Online Course

Who refers whom? The effects of teacher characteristics on disciplinary office referrals

Noncognitive Factors and Student Long-Run Success: Comparing the Predictive Validity of Observable Academic Behaviors and Social-Emotional Skills

A Quantitative Study of Mathematical Language in Classrooms

JUE Insight: From referrals to suspensions: New evidence on racial disparities in exclusionary discipline

Measuring Teaching Practices at Scale: A Novel Application of Text-as-Data Methods

The short- and long-run impacts of secondary school absences

Projecting the Potential Impact of COVID-19 School Closures on Academic Achievement

More Than Shortages: The Unequal Distribution of Substitute Teaching

Measuring Teaching Practices at Scale: A Novel Application of Text-as-Data Methods

Collaborators (2)

Ying Shi

Heather Hill

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