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Session Type: Paper Session
This session discusses the diversified application of interdisciplinary techniques in educational measurement. In this session, techniques discussed include meta coding, natural language processing methods, deep learning algorithms, automatic scoring, and data mining techniques.
Utilizing Deep Learning Language Models to Analyze Preservice Teachers' Written Reflections - Peter Wulff, University of Education - Heidelberg; Anna Nowak, University of Potsdam; Lukas Mientus, University of Potsdam; Andreas Borowski, University of Potsdam
An Exploration of an Integrated Approach for Enemy Item Identification - Xia Mao, NBOME; Qiuming Zhang, Edwards Lifesciences; Andrea Clem, NBOME
Meta-Coding: Unpacking Undetected Disagreement Among Coders During Observational Analysis - Paulina Biernacki, Stanford University; Guillermo Solano-Flores, Stanford University; Min Li, University of Washington; Maria Araceli Ruiz-Primo, Stanford University; Nixi Wang, University of Washington - Seattle; Dongsheng Dong, University of Washington - Seattle
Analyzing Automated Content Scoring for Knowledge Integration in Science Explanations Using Saliency Maps - Brian Riordan, ETS; Sarah Bichler, University of California - Berkeley; Allison Bradford, University of California - Berkeley; Marcia Linn, University of California - Berkeley
Exploring Patterns of Online Instruction Usage for K–5 Students Using k-Means Clustering - Elizabeth Adele Patton, Curriculum Associates