Digital Module 41: Process Data

Abstract Process data, such as log files from digital assessments, provide detailed records of how examinees interact with assessment tasks. These data offer opportunities to study test‐taking behavior, strategy use, and human‐machine interaction in ways that final item scores alone cannot capture. This module introduces the structure and characteristics of process data in large‐scale digital assessments and presents several approaches for transforming raw action sequences into numerical features that can be used in statistical and psychometric analyses. The module covers both expert‐derived and data‐driven feature extraction methods, including pattern‐based indicators, n‐grams, multidimensional scaling, and sequence autoencoders. A hands‐on section demonstrates data wrangling and feature extraction in R using the PISA 2012 Climate Control item and the ProcData package. The final section presents case studies showing how process‐derived features can be used to study test accommodations, improve measurement precision, and reduce and interpret differential item functioning. By the end of the module, learners should have a practical introduction to process data and a foundation for incorporating process‐derived information into educational measurement research.