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Session Type: Paper Session
Response process data provides an additional source of information to the outcome data in understanding test takers' problems solving process. In this session, new modeling techniques are proposed to analyze a variety of response process data, including response time, eye-movement and log file data.
The Speed-Accuracy Hierarchical Models for TIMSS 2019 Math Items - Jihang Chen, Boston College; Zhushan Mandy Li, Boston College; Matthias von Davier, Boston College
Evaluating Consistency of Behavioral Patterns Across Multiple Tasks Using Process Data: An Empirical Study in the Program for the International Assessment of Adult Competencies - Qiwei He, Georgetown University; Dandan Liao, McKinsey & Company; Hok Kan Ling; Hong Jiao, University of Maryland
One-Parameter Dynamic Choice Measurement Model for Process Data - Yue Xiao, Beijing Normal University; Hongyun Liu, Beijing Normal University
Sequential Response Model With Covariates (SRM-C): Assessing Item Recovery in Process Data Analysis - Yuting Han, Beijing Language and Culture University; Hongyun Liu, Beijing Normal University
Understanding Simulation-Type Items Using Eye Movement and Log-File Data - Paula Lehane, Dublin City University; Michael O'Leary, Dublin City University; Darina Scully, Dublin City University