Identifying Meaningful Patterns with Sequence Mining on Process Data in Large-Scale Assessments
Friday, February 27, 2026 | 10:30 a.m. – 12:00 p.m.
Scarfe Library Block Room 278 or Zoom
Speaker
Dr. Qiwei (Britt) He
Provost’s Distinguished Associate Professor
Director of AI Measurement and Data Science Lab
Georgetown University
Abstract
Computer-based assessments offer unprecedented opportunities to capture rich, granular log data through human–machine interactions, providing deeper insights into test-takers’ strategies and behaviors. This multidimensional sequential log data, encompassing actions, time intervals, eye-tracking, and more, poses challenges for traditional unidimensional sequence models. This talk presents an overview of sequence mining techniques applied to unstructured process data, with the goal of advancing item design and measurement frameworks. Two case studies are highlighted to illustrate the application of sequence mining in educational assessments. The first explores missing response patterns in a computational thinking assessment using Dynamic Time Warping (DTW) method, identifying potential causes of nonresponse and predicting missing values based on process data. The second investigates behavioral features of resilient students in scientific inquiry tasks, applying the Time-Warped Longest Common Subsequence (T-WLCS), a method originally developed for musical retrieval—to uncover interpretable patterns in student interactions. The presentation will also discuss the broader potential of integrating AI techniques with log data in large-scale assessments, exploring opportunities for more adaptive, inclusive, and behaviorally informed measurement systems.
About the Speaker
Dr. Qiwei (Britt) He is Provost’s Distinguished Associate Professor in the Data Science and Analytics Program at Graduate School of Arts and Sciences, and Founder and Director of the AI-Measurement and Data Science Lab at Georgetown University. Her research interests are broadly situated in the field of psychometric modeling and machine learning, with specific devotion to methodology advancement in sequential process data analysis, sequence mining, text mining, multimodal data analytics, artificial intelligence for interactive design, automated item generation and scoring systems in national and international large-scale assessments. Dr. He was appointed as Huges Hall Visiting Fellow at University of Cambridge (2025-2026), OECD Thomas J. Alexander Fellow (2017-2019), and currently serves on the Board on Human-System Integration at the National Academies of Science, Engineering and Medicine in the United States, and the joint committee for the revision of the Standards for Educational and Psychological Testing. Prior joining Georgetown, Dr. He was a Senior Research Scientist at Educational Testing Service (ETS) for over nine years. She was the recipient of the U.S. National Council on Measurement in Education (NCME) Annual Award of Exceptional Achievement in 2023, the NCME Jason Millman Promising Measurement Scholar Award in 2019, and the NCME Alicia Cascallar Outstanding Paper Award in 2017.
Please kindly complete the RSVP form if you plan to attend: