Shiyu Wang

Areas of Expertise

  • Latent Variable Modeling
  • Computerized Adaptive Testing
  • Cognitive Diagnosis Models
  • Educational Statistics

Interests

  • Multistage Adaptive Testing
  • Restrictive Latent Class Modeling
  • Adaptive Learning
  • Longitudinal Analysis
  • Dynamic Learning Models

Academic Affiliations

Education

  •  PhD in Statistics, 2016
    University of Illinois at Urbana-Champaign
  •  BS in Statistics, 2011
    Beijing Normal University

Contact

 706-542-3717 (office)

Research Summary

My research focuses on methodological innovations and advances in three areas: 1) developing innovative adaptive testing designs that can provide efficient individualized assessments and an examinee-friendly testing environment; 2) establishing statistical foundations for a family of restricted latent class models to provide guidelines for model estimation and selection; and 3) developing novel dynamic psychometric models that can measure and predict students’ learning outcomes based on various assessment data, including product data (i.e., students’ responses) and process data (i.e., response time and learning time).

I am looking for self-motivated master and Ph.D. students to join my research group in Fall 2022.

Grants

Bayesian Inference for Attribute Hierarchy in Cognitive Diagnosis Models
2021-2023
This research project will advance statistical methods for estimation and inference on attribute hierarchy within the framework of cognitive diagnosis models (CDM). CDMs have been widely applied to the field of educational assessment, psychiatric diagnosis, and other social sciences. In conjunction with diagnostic assessments, this type of model uses subjects’ observed responses to specifically designed diagnostic items to determine the fine-grained classification of the underlying latent attribute patterns. Attribute hierarchy, or the relationship among attributes, plays an important role in designing an effective diagnostic assessment. However, there is a lack of efficient statistical tools for estimating attribute hierarchy from observed data. This project will develop a series of Bayesian approaches for estimating attribute hierarchy. The project will contribute to the newly developed interdisciplinary field that integrates artificial intelligence with psychometrics. The new methods will be useful for applied research in education and psychology, as well as other social science disciplines. The investigators will apply the new methods to educational data sets. Graduate students will participate in the conduct of this research, and publicly available software will be developed.

Publications

Selected Publications

Adaptive Weight Estimation of Latent Ability: Application to Computerized Adaptive Testing with Response Revision.
  • Shiyu Wang, Houping Xiao and Allan Cohen
  • Journal of Educational and Behavioral Statistics

Using Response Times and Response Accuracy to Measure Fluency Within Cognitive Diagnosis Models
  • Shiyu Wang and Yinghan Chen
  • Psychometrika, 85(3), 600-629.

Awards and Accolades

Norton Prize for Outstanding Doctoral Thesis in Statistics

University of Illinois at Urbana-Champaign, 2015

Early Career Faculty Research Award

University of Georgia, 2018

Early Career Researcher Award

International Association for Computerized Adaptive Testing, 2019

NAEd/Spencer Postdoctoral Fellow

National Academy of Education and Spencer Foundation, 2019

Jason Millman Promising Measurement Scholar Award

National Council on Measurement in Education (NCME), 2020