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Session Type: Paper Session
This session explores cutting-edge developments in computer-adaptive testing (CAT) and related technologies in educational assessment. Studies collectively demonstrate the potential of advanced psychometric models, adaptive algorithms, and digital platforms to improve educational measurement. The session highlights both theoretical advancements and practical implications for large-scale assessment, providing insights into the future of educational testing and its role in supporting personalized learning and instruction.
A Latent Markov Model for Interaction Log Analytics - Hyeon-Ah Kang, University of Texas at Austin
Computerized Adaptive Testing’s Alignment With Assessment Guidelines: A Systematic Literature Review - Amirezza Mehrabi, Purdue University; Jason W Morphew, Purdue University
Computer Scoring of a Visual-Motor Assessment - Randy Fall, Azusa Pacific University
Two-Phase Content-Balancing CD-CAT Online Item Calibration - Jing Huang, Purdue University; Yuxiao Zhang, Purdue University; Jason W Morphew, Purdue University; Jayson Nissen, Montana State University; Ben Van Dusen, Iowa State University; Hua-Hua Chang, Purdue University