Dr. Kaiwen Man is an Associate Professor in the College of Education at the University of Alabama. His research lies at the intersection of statistics, psychometrics, and educational measurement, with a particular focus on developing innovative statistical models for Technology-Enhanced Digital Assessments (TEDA). Drawing on multimodal data sources such as eye-tracking, response times, and biometric indicators, his work advances methods for detecting aberrant test-taking behaviors, enhancing test security, and improving the validity of assessments.
Dr. Man’s contributions to multimodal psychometrics and test security have been recognized with multiple national awards from the National Council on Measurement in Education, including the Brenda Loyd Outstanding Dissertation Award and the Alicia Cascallar Research Award. He has published over 30 peer-reviewed articles—many as first author—in leading journals such as Educational and Psychological Measurement and Journal of Educational Measurement.
Beyond his research, Dr. Man is an active leader in the educational measurement community, serving as Chair of the Cognition and Assessment Program at AERA, Co-Chair of the NCME Test Security SIG.
Dr. Matthew J. Madison is an Associate Professor in the Department of Educational Psychology at the University of Georgia, where he also serves as Director of the Quantitative Methods Consulting Center. Dr. Madison currently serves as Vice Chair of the AERA Cognition and Assessment SIG and Chair of the NCME Diagnostic Measurement SIG.
His research focuses on psychometrics, diagnostic classification models, and longitudinal measurement, with particular emphasis on advancing statistical models to better evaluate growth, learning progressions, and intervention effects. He has published in leading journals including Psychometrika, Journal of Educational and Behavioral Statistics, Journal of Educational Measurement, Educational and Psychological Measurement, and Multivariate Behavioral Research.
Dr. Madison is the recipient of multiple national awards, including the AERA Cognition and Assessment SIG Outstanding Dissertation Award (2019) and the NCME Outstanding Service Award (2025). He has developed widely used psychometric software, such as the TDCM R package for estimating longitudinal diagnostic classification models, and regularly leads national and international training workshops on diagnostic measurement.
For more information about Dr. Madison’s publications and research projects, visit: www.matthewmadison.com.
Dr Jihong Zhang is currently a tenure-track Assistant Professor of Educational Statistics and Research Methods in the Department of Counseling, Leadership, and Research Methods and the Center for Public Health & Technology Research at the University of Arkansas. His main research areas are educational measurement, advanced statistical modeling, and AI-enhanced assessment in the fields of education and psychology, with a particular focus on psychological network modeling, Bayesian latent variable modeling, and Item Response Theory. He has been involved in multiple large-scale assessment and research projects, including the Kansas Assessment Program’s Dynamic Learning Maps initiative and digital learning assessment projects at SRI International. He has published in journals such as Educational and Psychological Measurement, Behavior Research Methods, and other leading outlets in psychometrics, statistics, and education, and has presented extensively at the American Educational Research Association (AERA) and the National Council on Measurement in Education (NCME).
Dr. Peida Zhan is an Associate Professor in the Department of Psychology at Zhejiang Normal University. His research primarily explores both theoretical and applied advancements in latent variable modeling, including cognitive diagnosis models, process data modeling (such as action sequences, response times, and action durations), and longitudinal modeling. He is also passionate about Bayesian methods, which form the second pillar of my research, focusing on Bayesian estimation of psychometric models.