Chun Wang

When the constructs that educational assessments try to measure are multifaceted, whether by design or not, multidimensional item response theory (MIRT), also known as item factor analysis, provides a unified framework and convenient statistical tool for item analysis, calibration, and scoring. The advancement of computational and statistical techniques has allowed for increased usage of MIRT models, but even with state-of-the-art algorithms, the computation can still be time-consuming, espe...

The purpose of this project was to investigate different methods of using a two-stage framework to address measurement error in the estimation of latent theta scores obtained from standardized tests through item response theory (IRT). In the two-stage approach, an appropriate measurement model is first fitted to the data, and the resulting theta scores are used in subsequent analysis. Potential benefits of this approach include clearer definition of factors, convenience for secondary data an...

National and international large-scale assessments (LSA) continue to serve as credible, independent, gold-standard yardsticks to assess what students know and can do in various subject areas. This proposal aims to contribute to the practitioner's toolkit for assessment analysis and score reporting by providing innovative variational regularization methods, software, and guidelines for the two critical steps in LSA analyses: Multidimensional Item Response Theory (MIRT) calibration and generat...

Generative artificial intelligence (GenAI) has great potential to augment teachers' effectiveness and personalize learning opportunities to enhance both the quality and equity of student learning outcomes. Nevertheless, the content generated by general-purpose AI tools has shortcomings, with limited conceptual depth and inadequate understanding of pedagogical principles and student learning. Consequently, schools and districts are concerned about overreliance on technology as a replacement f...