Analyzing Complex Educational Data: A Data Analytic Framework for Integrating Structured and Unstructured Eye-Tracking Data

doi:10.1017/psy.2026.10096

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Analyzing Complex Educational Data: A Data Analytic Framework for Integrating Structured and Unstructured Eye-Tracking Data

2026-03-01

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DOI：10.1017/psy.2026.10096

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Abstract

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The growing use of computer-based assessments has produced complex process data that capture learners' cognitive and behavioral processes in real time. Among these, eye-tracking data provide rich temporal information on how individuals attend to and process visual information during problem solving. Yet, analyzing such high-dimensional, temporally dependent, and multimodal data remains a methodological challenge. This study introduces a two-component data-analytic framework (DAK) for integrating and interpreting structured and unstructured data in educational assessments. The first component employs a time-aware long short-term memory Autoencoder to extract latent features representing dynamic visual attention patterns. The model extends conventional architectures by incorporating fixation duration and elapsed time between actions, using a data-driven temporal decay function, and optimizing a multi-target reconstruction objective. The second component integrates these extracted features through clustering, categorical data analyses, and mixed-effects modeling to generate construct-relevant validity evidence for test-taking and learning behaviors. We demonstrate the DAK using structured scores and unstructured eye-tracking data from a spatial rotation learning program. Results reveal distinct behavioral patterns linked to test performance and intervention effectiveness, highlighting the potential of multimodal process data to advance psychometric modeling and instrument design.

Keywords:

eye-tracking data

feature extraction

high-dimensional data

pattern recognition

process data