Shiyu Wang College of Education University of Georgia Speaker's Website Thu, 02/09/2023 - 4:00pm 204 Caldwell Hall Event Flyer (468.76 KB) Exploratory Cognitive Diagnosis Models: Attribute Hierarchy Estimation and Exploration of Utilizing Eye-tracking Data. Abstract: Attribute hierarchy, the underlying prerequisite relationship among attributes, plays an important role in applying Cognitive Diagnosis Models (CDM) for designing efficient cognitive diagnostic assessments. However, there are limited statistical tools to directly estimate attribute hierarchy from response data. In this talk, we present a Bayesian formulation for a class of general CDMs with attribute hierarchy and introduce an efficient Metropolis within Gibbs algorithm to estimate the underlying hierarchy along with CDM parameters. Our simulation study demonstrated our method can fully recover or estimate at least a subgraph of the underlying structure across various conditions. The real data application indicates the potential of learning attribute structure from data using our algorithm and validating the existing attribute hierarchy specified by content experts. In the end of the talk, we will also briefly present some preliminary results of one on-going project that aim to use eye-tracking data to improve the estimation of CDMs. Biography: Dr. Shiyu Wang is an Associate Professor from Quantitative Methodology Program, Department of Educational Psychology at University of Georgia. Before moving to this position, she got her PhD in statistics from University of Illinois at Urbana-Champaign. Dr. Wang’s research interests lie broadly to personalized assessment and learning through three perspectives: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 a variety of assessment data, including produce data (i.e., students’ responses) and process data (i.e., response time and learning time) to facilitate the development of adaptive learning. Dr. Wang actively publishes within the top journals in psychometrics, educational measurement, and statistics. She was selected as the 2019 NAEd/Spencer post-doctoral fellow. She received the 2019 IACAT early career researcher award and the 2020 Jason Millman Promising Measurement Scholar Award from the National Council on Measurement in Education.