College of Education and Human Development

Department of Educational Psychology

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 the development of innovative psychometric methods to enhance measurement practices in education and psychology, along with the application of 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 research centers on the development of innovative psychometric methods to enhance measurement practices in education and psychology, along with the application of measurement techniques to survey and scale development and validation.

        A central focus of my recent methodological work is 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.

        My applied research is highly interdisciplinary—I collaborate with scholars in fields such as special education, STEM and language education, and psychiatry 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.

        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 accessibility of the package, 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), as well as the 2018 Outstanding Dissertation Award from AERA Division D (Measurement & Research Methodologies).

        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

          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.

          Dr. Wenchao Ma headshot

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

            Wenchao Ma CV

          • wma@umn.edu