Analysis of Log Data From an International Online Educational Assessment System: A Multi-State Survival Modeling Approach to Reaction Time Between and Across Action Sequence

With increasingly available computer-based or online assessments, researchers have shown keen interest in analyzing log data to improve our understanding of test takers' problem-solving processes. In this article, we propose a multi-state survival model (MSM) to action sequence data from log files, focusing on modeling test takers' reaction times between actions, in order to investigate which factors and how they influence test takers' transition speed between actions. We specifically identify the key actions that differentiate correct and incorrect answers, compare transition probabilities between these groups, and analyze their distinct problem-solving patterns. Through simulation studies and sensitivity analyses, we evaluate the robustness of our proposed model. We demonstrate the proposed approach using problem-solving items from the Programme for the International Assessment of Adult Competencies (PIAAC).

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Publication Details

Journal
Psychometrika
Published
2025-09-01
DOI
https://doi.org/10.1017/psy.2025.10043
Primary Topic
Cognitive Science and Mapping
Type
article
Field-Weighted Citation Impact
0.00

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article

Analysis of Log Data From an International Online Educational Assessment System: A Multi-State Survival Modeling Approach to Reaction Time Between and Across Action Sequence

Ick Hoon Jin, Minjeong Jeon, Jina Park
Psychometrika
Cognitive Science and Mapping
article

Analysis of Log Data From an International Online Educational Assessment System: A Multi-State Survival Modeling Approach to Reaction Time Between and Across Action Sequence

Ick Hoon Jin, Minjeong Jeon, Jina Park
article en

Abstract

With increasingly available computer-based or online assessments, researchers have shown keen interest in analyzing log data to improve our understanding of test takers' problem-solving processes. In this article, we propose a multi-state survival model (MSM) to action sequence data from log files, focusing on modeling test takers' reaction times between actions, in order to investigate which factors and how they influence test takers' transition speed between actions. We specifically identify the key actions that differentiate correct and incorrect answers, compare transition probabilities between these groups, and analyze their distinct problem-solving patterns. Through simulation studies and sensitivity analyses, we evaluate the robustness of our proposed model. We demonstrate the proposed approach using problem-solving items from the Programme for the International Assessment of Adult Competencies (PIAAC).

PsychometrikaVol. 90(4)
University of California, Los Angeles (US), Yonsei University (KR), University of California System (US)
National Research Foundation, Yonsei University, National Research Foundation of Korea
Openalex Percentile: Top 88%
Cognitive Science and Mapping
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