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Advancing Educational Research for All with Large-Scale School Assessment Data: Insights from Innovative Approaches
Sun, April 12, 9:45 to 11:15am PDT (9:45 to 11:15am PDT), InterContinental Los Angeles Downtown, Floor: 5th Floor, Boyle HeightsSession Type: Symposium
Abstract
Innovative approaches such as process data analytics, response-time modeling, and artificial intelligence applications are shaping the future of educational research. These approaches enhance the use of large-scale school assessment data to understand student engagement, improve measurement, and provide educators and researchers with meaningful feedback. By advancing methods for data analysis and interpretation, these approaches support more equitable and actionable educational practices. Papers in this symposium examine how emerging analytic techniques are applied across national and international assessments to improve the quality, relevance, and usability of assessment data. Together, the studies demonstrate how methodological innovations can deepen insights into teaching and learning—promoting stronger connections between research, evaluation, and practice in school settings for all students.
Sub Unit
Chair
Papers
Identifying Navigation Behavioral Patterns with Time Warped Longest Common Subsequence on Process Data - Qiwei He, Georgetown University; Binhui Chen, Georgetown University
Preliminary Development of a Mathematics Engagement Composite Using NAEP Process Data of Eighth-Grade Students - Antranik T Kirakosian, Washington State University
Response Time-Informed Multiple Imputation: Comparing IRT-, MICE-, and Autoencoder-Based Approaches for Planned Missing Data - Surina He, University of Alberta; Peter van Rijn; Usama S. Ali, Educational Testing Service
Toward Human-Centered Explainable AI Applications for Generating Feedback for Classroom Teachers from Large-Scale Assessments - Hongwen Guo, Educational Testing Service; Matthew S. Johnson, Educational Testing Service; Luis Enrique Saldivia, ETS; Michelle Worthington, ETS; Jeremy Lee, ETS; Kadriye Ercikan, Educational Testing Service
Comparative Analysis on Item Difficulty Predictions Between National and International Assessment Programs - Mubarak O. Mojoyinola, University of Iowa; Olasunkanmi James Kehinde, Norfolk State Uinversity; Judy H. Tang, Westat