报告人：Kaiwen Man
报告地点：腾讯会议ID:384-438-849
报告时间：2022年12月15日星期四9:00-10:00
报告摘要:
Many important high-stakes decisions—college admission, academic performance evaluation, and even job promotion—depend on accurate and reliable scores from valid large-scale assessments. However, test takers sometimes behave aberrantly (e.g., cheating and guessing), which can undermine the effectiveness of such assessments in yielding accurate, precise information of examinees’ performances. Given this context, the purpose of this research is to create, develop, investigate, and test new statistical approaches that would jointly integrate bio-information (i.e., data) collected from eye-tracking machines with item responses and response time data, which later could be used to detect fraudulent testing behaviors in large-scale testing scenarios. In the current study, three negative binomial models will be introduced for modeling visual fixation counts of test takers solving a set of items. Also, a trivariate joint modeling approach will be introduced that integrates item responses, reaction time and visual fixation counts. This joint model is a cast as a multilevel model in which the structural relationship among the responding accuracy, the working speed and the attention would be manifested. Finally, extensions of using these models for aberrant behavior detections would be discussed. A Markov chain Monte Carlo estimation method is implemented for parameter estimation.
主讲人简介：
Dr. Kaiwen Man was appointed as an Assistant Professor in the Department of Educational Studies in Psychology, Research Methodology, and Counseling at the University of Alabama. Also, Kaiwen has held many positions as Researchers including at the Association of American Medical College, at the Educational Testing Service, and at the Charted Financial Analyst.
His research explores questions on the boundaries and interactions of the educational statistics, biometrics, and behavioral research literature with particular attention to models for eye-tracking data, responding process data, Bayesian statistics, and data mining. His works has been published in many peer-reviewed flagship quantitative journals such as Educational and Psychological Measurement, Journal of Educational Measurement, Journal of Educational and Behavioral Statistics, and Applied Psychological Measurement. Moreover, He received the 2022 Brend H. Loyd outstanding dissertation award from National Council on Measurement in Education (NCME). Furthermore, his projects have been externally-funded by the ETS Harold Gulliksen Psychometric Research Fellowship program and the Institute of Education Science.
Kaiwen holds a Ph.D. in Measurement, Statistics, and Evaluation from the University of Maryland, College Park, double Masters’ degrees in Economics and Mathematical Statistics at the University of Illinois at Urbana-Champaign. He also earned his double Bachelors’ degrees in Economics and Psychology at Lanzhou University (China).
