People

Wenchao Ma

Pronouns: He, him
Associate professor, the American Guidance Service Inc. and John P. Yackel Professor in Educational Assessment and Measurement

My research centers on developing innovative psychometric methods to enhance measurement practices in education and psychology, as well as applying measurement techniques to survey and scale development and validation.

DEGREES

PhD, education, Rutgers, The State University of New Jersey, USA
MS, statistics, Rutgers, The State University of New Jersey, USA
MEd, developmental and educational psychology, Beijing Normal University, China
BS, psychology, Beijing Normal University, China

AREAS OF INTEREST

Cognitive diagnosis models
Item response theory
Latent class analysis
Factor analysis
Machine learning and AI for measurement
Scale development and validation
Reliability and validity of test scores

I am currently accepting doctoral students in the Quantitative Methods in Education (QME) program. Students with research interests that align with mine are encouraged to apply.

BIOGRAPHY

My applied research is highly interdisciplinary—I collaborate with scholars in fields such as psychiatry, public health, special education, STEM (science, technology, engineering, and mathematics), and language education to address complex challenges in measurement and assessment across both traditional formats and interactive or game-based platforms. These collaborations draw on a range of statistical, psychometric, and computational approaches to improve the design, implementation, and interpretation of assessments. Through this work, I aim to apply cutting-edge measurement techniques to support evidence-based decision-making and advance educational practice.

My recent methodological work focuses on cognitive diagnosis models (CDMs), which are grounded in item response theory (IRT) and latent class analysis. My goal is to develop rigorous psychometric toolsets that support personalized learning and data-informed instructional decision-making.

Over the past several years, I have been developing and maintaining a number of software packages to support psychometric research and practice. One example is the GDINA R package, which offers a comprehensive suite of tools for cognitive diagnosis, including model estimation, fit evaluation, differential item functioning analysis, and reliability assessment. To enhance the package's accessibility, I also developed a graphical user interface. More information about these projects is available on my GitHub.

My work has been recognized by several national awards, including the 2021 Jason Millman Promising Measurement Scholar Award and the 2017 Bradley Hanson Award for Contributions to Educational Measurement, both presented by the National Council on Measurement in Education (NCME), and the 2018 Outstanding Dissertation Award from AERA Division D (Measurement and Research Methodologies).

Psychometrics and Data Analytics (PANDA) Lab

Courses I teach:

EPSY 5221: Principles of Educational and Psychological Measurement
EPSY 5244: Survey Design, Sampling and Implementation
EPSY 8252: Statistical Methods in Education II

PUBLICATIONS

Latif, E., Ma, W., Zhai, X. (2026). Intelligent tutoring systems for STEM learning. In: Zhai, X., Lee, G. (Eds.) Artificial Intelligence for STEM Education Research. Advances in Technology-Rich Science Education. Springer, Cham. https://doi.org/10.1007/978-3-032-06565-0_8

Ma, W., Preast, J.L., Sanders, S., Hester, O.R., Jolivette, K., Shelton, S.A., Odom, K.P., Prewitt, N.B. and Pitzel, A. (2026). Psychometric properties of the modified Illinois bully/victim/fight scale for youth in restrictive education settings. Assessment for Effective Intervention, https://doi.org/10.1177/15345084261434199

Lugu, B., Guo, W., Ma, W. (2025). A cognitive diagnosis model for disengaged behaviors. Behavior Research Methods. https://doi.org/10.3758/s13428-025-02734-y

Rajeb, M., Ma, W., He, Q., & Shi, Q. (2025). Incorporating process information into cognitive diagnostic models: A four-component joint modeling approach. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/10769986251334788

Zhai, X., Nyaaba, M., & Ma, W. (2025). Can AI outperform humans on cognitive-demanding problem-solving tasks in science? Science & Education, 34, 649-670. https://doi.org/10.1007/s11191-024-00496-1

Ravand, H., Effatpanah, F., Ma, W., de la Torre, J., Baghaei, P., Kunina-Habenicht, O. (2025). Exploring interrelationships among L2 writing subskills: insights from cognitive diagnostic models. Applied Measurement in Education. https://doi.org/10.1080/08957347.2024.2424550

Luo, F., Liu, R., Nasrin, F., Awoyemi, I. D., Crawford, C., & Ma, W. (2024). Engaging students of color in physiological computing with insights from eye-tracking. Journal of Research on Technology in Education, 1-22. https://doi.org/10.1080/15391523.2024.2381226

Ma, W., Sorrel, M. A., Zhai, X. & Ge, Y. (2024). A dual-purpose model for binary data: Estimating ability and misconceptions. Journal of Educational Measurement, 61, 179-197. https://doi.org/10.1111/jedm.12383

Zhai, X., Haudek, K. & Ma, W. (2023). Assessing argumentation using machine learning and cognitive diagnostic modeling. Research in Science Education, 53, 405-424. https://doi.org/10.1007/s11165-022-10062-w

Yu, J., Ma, W., Moon, J., & Denham, A. R. (2022). Developing a game learning analytic system using a continuous conjunctive model. Journal of Learning Analytics, 9(3), 11-31. https://doi.org/10.18608/jla.2022.7639

Complete list of publications available at Experts@Minnesota or my Google Scholar.

Department of Educational Psychology
167 Education Sciences Bldg.
56 East River Road
Minneapolis, MN 55455

Wenchao Ma CV
wma@umn.edu