Analyzing Process Data from Computer-Based Assessments: A Tutorial on Preprocessing, Feature Extraction, and Model-Based Inference

TL;DR

This paper presents a comprehensive framework for preprocessing, analyzing, and modeling process data from computer-based assessments, demonstrated through the PIAAC PS-TRE domain, with reproducible R code provided.

Contribution

It offers an end-to-end analytical pipeline including preprocessing, feature extraction, and model-based inference for process data, filling a gap in systematic guidance and cross-method consistency.

Findings

N-gram behavioral clusters reveal diagnostic differences among incorrect respondents.

Multidimensional scaling features effectively reconstruct behavioral variables.

Process-informed DIF analysis helps identify and reduce construct-irrelevant group differences.

Abstract

Computer-based assessments routinely generate detailed interaction logs -- commonly referred to as process data -- that record every action a respondent performs during task completion, yet systematic preprocessing guidance, integrated analytical workflows, and cross-method consistency checks remain scarce in the literature. This paper provides a unified, end-to-end analytical framework for analyzing process data from large-scale assessments -- covering the full pipeline from raw log preprocessing to model-based inference -- using the Programme for the International Assessment of Adult Competencies (PIAAC) Problem Solving in Technology-Rich Environments (PS-TRE) domain as an illustrative example. We first present a systematic preprocessing pipeline -- including timestamp correction, duplicate removal, action block consolidation, and LLM-assisted standardization -- that transforms raw event-level logs into analysis-ready action sequences. We then review and demonstrate two complementary families of analytical methods. The first consists of feature-based methods and their downstream applications, including descriptive process indicators, n-gram analysis with TF--IDF weighting, multidimensional scaling, and process data-informed differential item functioning (DIF) analysis. The second consists of model-based approaches, namely hidden Markov models and the subtask identification procedure. Empirical illustrations using the United States sample illustrate that n-gram-based behavioral clusters carry differential diagnostic information primarily among incorrect respondents, that multidimentionsl scaling-derived features comprehensively reconstruct observed behavioral variables, and that process-informed DIF analyses can identify and mitigate construct-irrelevant sources of group differences. Reproducible R code implementations are provided for all major techniques.