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Innovative Methodologies in Item Response Theory (Table 20)
Sat, April 13, 1:15 to 2:45pm, Pennsylvania Convention Center, Floor: Level 200, Exhibit Hall BSession Type: Roundtable Session
Abstract
This session brings contemporary methodologies into educational and social science research, with a focus on Item Response Theory (IRT) and its applications, including Artificial Intelligence (AI) in scoring, survey of student experiences and college rating, multidimensional dynamic latent characters, and multidimensional science learning.
Sub Unit
Chair
Papers
AI Model Evaluation With the Proportional Reduction in Mean Squared Error - Daniel F. McCaffrey, Educational Testing Service; Jodi M. Casabianca, Educational Testing Service; Matthew S. Johnson, Educational Testing Service
Latent Change Is Not a Unidimensional Characteristic: Evidence From IRTree Models - Denis Federiakin, Johannes Gutenberg University of Mainz; Olga Zlatkin Troitschanskaia, Johannes Gutenberg University of Mainz; William B. Walstad, University of Nebraska - Lincoln
Don’t Rely on the Mean: Investigating Students’ Polytomous Belongingness Scores Using Explanatory Item Response Models - Pearl Lo, University of Maryland
Investigation of Generalized Partial Credit Models and the Relationship Between College Choice and Students' Ratings - Margeau Jong, Boston College; Zhushan Mandy Li, Boston College
Measuring the Impact of Multidimensional Science Instructional Materials - Cari F. Herrmann-Abell, BSCS Science Learning; Jeffrey Snowden, BSCS Science Learning; Molly A.M. Stuhlsatz, BSCS Science Learning; Brian M. Donovan, University of Colorado - Boulder; Cynthia M. Passmore, University of California - Davis; Patricia Olson, BSCS Science Learning; Christopher D. Wilson, Biological Sciences Curriculum Study