Digital Module 41: Process Data

Susu Zhang

doi:10.1111/emip.70030

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Digital Module 41: Process Data

2026-05-22

张夙夙 (Susu Zhang) *

Qiwei He

Sunbeom Kwon

DOI：10.1111/emip.70030

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Abstract

En 中文

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.

Keywords:

action sequences

educational measurement

feature extraction

large-scale assessment

log data

process data

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