Speaker: Dr. Jiawei Xiong, Associate Researcher Scientist at Pearson
Title: Next-Generation Process Data Innovations with the introduction of Sequential Reservoir Model
Abstract: This talk centers on the analysis of assessment process data. There has been a growing interest in utilizing process data from log files in computerized assessments. This type of data, which captures examinees' response activities such as clickstreams during tests, provides insights into their engagement and problem-solving trajectories. This talk introduces a recent advanced model, Sequential Reservoir Model, based on data-driven approaches to extract interpretable features from unstructured process data. These features can clearly distinguish examinees’ behavior patterns. This innovative analysis of process data can help assess latent variable analysis in measurement, thereby providing a more comprehensive measurement of examinee performance.
Brief biography: Dr. Jiawei Xiong holds a Ph.D. in Quantitative Methodology and an M.S. in Statistics from the University of Georgia, complemented by an undergraduate degree in Engineering. His research focuses on developing cutting-edge data science and machine learning methods for educational and psychological statistics, particularly in process data, item response theory, and educational data mining. His dissertation on process data investigation was awarded the 2023 NCME Dissertation Award. Currently, he serves as an Associate Research Scientist at Pearson and a Research Affiliate at the University of Georgia, while also leading as the Chair of the AERA Division D International Committee.