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Steven C. Beering Hall of Liberal Arts and Education 100 N. University Street West Lafayette Indiana 47907-2098 BRNG 5166work Work Phone: (765) 494-9625work Work Email: chang606@purdue.eduINTERNET Website: Vita

Categories: Educational Psychology and Research Methodology, Educational Studies

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Hua-Hua Chang

Charles R. Hicks Chair Professor, Professor of Statistics (Courtesy) Educational Psychology & Research MethodologyEducational Studies

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Professor Chang came to Purdue in August 2018. Prior to joining Purdue, he worked at the University of Illinois at Urbana-Champaign (UIUC) and held professorships in Educational Psychology, Psychology and Statistics. Professor Chang has focused his research primarily on methodological developments to support testing and assessment. Since August 2018, he has been devoting his effort to build assessment tools to help teaching and learning. He emphasized in a recent publication: “Modern theories in measurement are rapidly transforming testing from unaccommodating measures into informative tools that can be used to address various challenges in today’s teaching and learning.” His most current research projects include developing AI-powered diagnostic tools to improve classroom assessment in large college gateway courses, and also developing personalized adaptive assessment to support individualized learning.

Chang, H-H., Wang, C., & Zhang, S. (2021). Statistical applications in educational measurement. Annual Review of Statistics and Its Application. https://doi.org/10.1146/annurev-statistics-042720-104044.
Li, X., Xu, H., Zhang, J., & Chang, H-H. (2021). Optimal hierarchical learning path design with reinforcement learning. Applied Psychological measurement. DOI: 10.1177/0146621620947171.
Kang, H., Zheng, Y., & Chang, H-H. (2020). Online calibration of a joint model of item responses and response times in computerized adaptive testing. Journal of Educational and Behavioral Statistics, 45, 175-208.
Zhang, S. & Chang, H-H. (2019). A multilevel logistic Hidden Markov model for learning under cognitive diagnosis. Behavior Research Methods. DOI 10.3758/s13428-019-01238-w. (With Student)
Choe, M. E. & Chang, H-H. (2019). The asymptotic distribution of average test overlap rate in computerized adaptive testing. Psychometrika, https://doi.org/10.1007/s11336-019-09674-5. (With Student)
Li, X., Zhang, J., & Chang, H-H. (2019). Look-ahead content balancing method in variable length computerized classification testing. British Journal of Mathematical and Statistical Psychology. DOI 10.1111/bmsp.12161. (With Student)
Wang, S., Fellouris, G., & Chang, H-H. (2019). Statistical foundations for computerized adaptive testing with revision. Psychometrika. 84 (2), pp 375–394. (With Student)
Tu, D., Wang, S., Douglas, J. & Chang, H-H. (2019). Cognitive diagnostic models with attribute hierarchies: Model estimation with a restricted Q matrix design. Applied Psychological Measurement, 43 (4), 255–271.
Lin, C.L., & Chang, H-H. (2019). Item selection criteria with practical constraints in cognitive diagnostic computerized adaptive testing. Educational and psychological Measurement, 79(2), 335–357.
Morphew, J., Mestre, J., Kang, H., Chang, H-H., & Fabry, G. (2018). Using computer adaptive testing to assess physics proficiency and improve exam performance. Physical Review Physics Education Research, DOI: 10.1103/PhysRevPhysEducRes.14.010127.
Chang, H.-H. (2015). Psychometrics behind computerized adaptive testing. Psychometrika, 80 (1), 1-20.
Chang, H-H., & Ying, Z. (2009). Nonlinear sequential designs for logistic item response theory models with applications to computerized adaptive tests. The Annals of Statistics, Vol 37, No. 3, 1466-1488.
Chang, H-H., & Ying, Z. (2008). To weight or not to weight? Balancing influence of initial items in adaptive testing. Psychometrika, 73 (3), 441-450.
McGlohen, M. & Chang, H-H. (2008). Combining computer adaptive testing technology with cognitively diagnostic assessment. Behavior Research Methods 40 (3), 808-821.