Increased use of computer-based assessments brings a great opportunity to track process data with the aim to gain a deeper insight about respondents’ test-taking behavioral patterns andproblem-solving strategies. The fine-grained process data are often in complex and multidimensional form that would need to be analyzed with an integration of data-driven analytic approaches in addition to classical psychometric models. In this talk, I will give an overview of sequence-based methods (e.g., n-grams, sequence distance computation, latent sequence extraction) applied in clickstream process data and present the accumulative explorations with sequence mining techniques on process data by item level and aggregate level, respectively. The goal of these studies is to leverage sequential process data in large-scale assessments to assist in understanding how respondents interact with the items administered, thus support test construction, enhance latent ability estimation, improve validity of conclusions, and facilitate cross-national comparisons.
About the speaker
Dr. Qiwei (Britt) He is a Senior Research Scientist in Psychometrics and Data Science Modeling at Educational Testing Service (ETS). She completed her PhD in Psychometrics and Data Science at University of Twente in 2013. Her research interests are broadly situated in the field of psychometric modeling and data mining, with specific focus on methodology advancement in unstructured data analysis (e.g., process data, textual data), sequence mining, text mining, machine learning, item response modeling, latent factor analysis, large-scale assessments, and interactive item design. She has been leading multiple research projects funded by NSF, IES, IEA, NCES and OECD in developing methods to analyze complex process data to understand respondents’ behaviors and cognitive process. Dr. He was appointed as OECD Thomas Alexander Fellow and selected into the Psychometrics and Educational Evaluation Panel for UNESCO Institute for Statistics. She was the recipient of Annual Award of Exceptional Achievement in 2023, Jason Millman Promising Measurement Scholar Award in 2019 and Alicia Cascallar Outstanding Paper of Early-Career Scholar Award in 2017 from the National Council on Measurement in Education.