Computerized Adaptive Testing: From Robbins-Monro to F. Lord and Beyond
报告人：张华华
报告地点：MK官方APP下载415报告厅
报告时间：2019年12月22日星期日09:00-11:00
邀请人：孟祥斌
报告摘要：
With current big data, AI and deep learning uprising, statisticians are at a crossroads whether to make a turn to the fashionable machine learning and deep learning. In Educational Testing, we have been using methods like AI and machine learning for decades. Today we are going to focus on Computerized Adaptive Testing (CAT) and show how it can be utilized to build a tailored assessment for each individual. Our goal is to build many reliable, and also affordable, web-based diagnostic tools for schools to automatically classify students' mastery levels for any given set of cognitive skills that students need to master. In addition, we will show how these tools can be employed to support individualized learning on a mass scale.
主讲人简介：
Dr. Hua-Hua Chang is the Charles R. Hicks Chair Professor in the Department of Educational Studies at Purdue University. Before joining Purdue in August 2018, he held professorships in Educational Psychology, Psychology and Statistics at the University of Illinois at Urbana-Champaign (UIUC). Dr. Chang’s interests are broad, encompassing both theoretical development and applied methodologies, including computerized adaptive testing, cognitive diagnosis, asymptotic properties in Item Response Theory, and statistically detecting item bias. Most recently, his work has been concentrated on developing web-based assessment tools to facilitate individualized learning. He twice served as Fulbright Specialist (Colombia, 2019 and Australia, 2005), he is a Fellow of the American Statistical Association (2019), a Fellow of American Educational Research Association (AERA, 2010), and the 2017 recipient of the E. F. Lindquist Award by AERA.
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