Recovery of Attribute Hierarchy in Cognitive Diagnosis Models

报告人：陈颖菡

报告地点：腾讯会议ID:211781657

报告时间：2023年10月15日星期日10:00-11:00

邀请人：陆婧

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. I will present a Bayesian formulation for CDMs with attribute hierarchy, and propose an efficient Metropolis within Gibbs algorithm to estimate the underlying hierarchy through transitive reductions of directed acyclic graphs (DAGs). Simulation studies demonstrated our method can estimate the whole graph or a subgraph of the underlying structure across various conditions. Applications to educational data indicate the potential of learning and validating hierarchical structures through the proposed method.

主讲人简介：

陈颖菡博士是内华达大学里诺分校数学与统计系副教授。她于 2017 年在伊利诺伊大学厄巴纳香槟分校获得统计学博士学位。她的研究兴趣包括高级的蒙特卡罗方法、贝叶斯统计、计算统计和心理测量学。她曾在《Psychometrika》、《Journal of Educational and Behavioral Statistics》、《Journal of Computational and Graphical Statistics》、《Applied Psychological Measurement》等统计和心理测量领域的权威学术期刊上发表论文。