Yi Zheng

Associate Professor

Arizona State University

United States

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About

Yi Zheng is an Associate Professor at Arizona State University in the United States. His research interests encompass Bayesian decision theory, student anxiety in large-enrollment online science courses, and environmental attitudes among adolescents. He has contributed to various academic discussions surrounding inclusive teaching strategies and student engagement in science, as well as addressing disparities in access to educational programs. His recent publications emphasize the intersection of education, psychology, and environmental studies.

Articles (10)

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2023

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2021

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2021

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2021

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2021

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2021

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2021

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2021

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2020

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2020

Collaborators (1)

Elizabeth Barnes

Assistant Professor

Middle Tennessee State University

UNITED STATES

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Yi Zheng

Research Interests

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Articles (10)

Bayesian decision theory for tree-based adaptive screening tests with an application to youth delinquency

A New Measure of Students’ Perceived Conflict between Evolution and Religion (PCoRE) Is a Stronger Predictor of Evolution Acceptance than Understanding or Religiosity

Aspects of Large-Enrollment Online College Science Courses That Exacerbate and Alleviate Student Anxiety

More is not always better: A study of country-level factors associated with adolescents’ environmental attitudes using a multilevel analysis of PISA 2006

Gender Differences in Student Comfort Voluntarily Asking and Answering Questions in Large-Enrollment College Science Courses

An Exploration across Institution Types of Undergraduate Life Sciences Student Decisions to Stay in or Leave an Academic-Year Research Experience

It’s in the syllabus … or is it? How biology syllabi can serve as communication tools for creating inclusive classrooms at a large-enrollment research institution

Call on me! Undergraduates’ perceptions of voluntarily asking and answering questions in front of large-enrollment science classes

Online biology degree program broadens access for women, first-generation to college, and low-income students, but grade disparities remain

Using Machine Learning Methods to Develop a Short Tree-Based Adaptive Classification Test: Case Study With a High-Dimensional Item Pool and Imbalanced Data

Collaborators (1)

Elizabeth Barnes

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