报告人：王诗宇
报告地点：腾讯会议ID:459-858-390
报告时间：2022年09月27日星期二9:30-10:30
报告摘要：
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.
主讲人简介：
王诗宇，佐治亚大学教育心理系量化方法方向副教授（https://people.coe.uga.edu/shiyu-wang/）本科就读于北京师范大学统计系，获得伊利诺伊大学香槟分校统计系博士。现在研究兴趣侧重于个性化测验和学习的研究，主要包括计算机自适应测试 (computerized adaptive testing)，多元化复杂行为数据的统计建模，包括 纵向数据(longitudinal data), 过程性数据 (process data, response time)，文本数据 (text data) 和各种测验的学生作答数据。已在Psychomerika, Journal of Educational Measurement, Journal of Educational Behavior and Statistics 和 Multivariate Behavioral Research 等多个测量和统计顶级杂志发表多篇论文。获得 2019 IACAT early career researcher award and the 2020 Jason Millman Promising Measurement Scholar Award from the National Council on Measurement in Education.
