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Process Data Modeling and Application in Educational Assessments
Sun, April 11, 2:30 to 4:00pm EDT (2:30 to 4:00pm EDT), Division D, Division D - Section 1 Paper and Symposium SessionsSession Type: Paper Session
Abstract
This session highlights the development of integrating process data into response modeling. This session consists of papers that use the process data to predict student performance or to better understand students' thinking, papers that analyze process data for different testing types, such as a computer science assessment and PISA, and paper that focuses on non traditional data types, such as ranking data.
Sub Unit
Papers
Understanding Predictors of Students' Performance in an Online Licensure Test Preparation Course - Dakota Wayne Cintron, University of California - San Francisco; Nina Deng
Nonconstruct Item Features and Response Processes in Computer Science Assessments: Evidence From Think-Alouds and Sequence Analysis - Matt Davidson, University of Washington - Seattle; Min Li, University of Washington
Multitrait Situation Reaction Tests - Warren E. Lacefield, Academic Software, Inc.; Brooks Applegate, Western Michigan University
An Investigation of Response Time and Response Behavior on PISA Science Assessment - Mingjia Ma, University of Iowa
Extracting, Structuring, and Analyzing Log File Data to Make Different Thinking Profiles Visible - Gyongyver Molnar, University of Szeged; Beno Csapo, University of Szeged