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

Luyang Fang1, Shiyu Wang2, Yinghan Chen3

  • 1Statistics, https://ror.org/00te3t702University of Georgia, USA.

Psychometrika
|March 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel data-analytic framework (DAK) to analyze complex eye-tracking and assessment data. The DAK reveals distinct behavioral patterns linked to learning and performance, advancing psychometric modeling.

Keywords:
eye-tracking datafeature extractionhigh-dimensional datapattern recognitionprocess data

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Area of Science:

  • Educational Technology
  • Psychometrics
  • Cognitive Science

Background:

  • Computer-based assessments generate complex, real-time process data.
  • Eye-tracking data offers rich temporal insights into visual information processing during problem-solving.
  • Analyzing high-dimensional, multimodal, and temporally dependent data presents significant methodological challenges.

Purpose of the Study:

  • To introduce a two-component data-analytic framework (DAK) for integrating and interpreting structured and unstructured data in educational assessments.
  • To extract latent features representing dynamic visual attention patterns from eye-tracking data.
  • To generate construct-relevant validity evidence for test-taking and learning behaviors using integrated multimodal data.

Main Methods:

  • Developed a time-aware long short-term memory Autoencoder incorporating fixation duration and elapsed time.
  • Employed a data-driven temporal decay function and optimized a multi-target reconstruction objective.
  • Integrated extracted features using clustering, categorical data analyses, and mixed-effects modeling.

Main Results:

  • Demonstrated the DAK using spatial rotation learning program data (structured scores and eye-tracking).
  • Identified distinct behavioral patterns associated with test performance.
  • Revealed behavioral patterns linked to intervention effectiveness.

Conclusions:

  • The DAK effectively integrates multimodal process data for advanced psychometric modeling.
  • Eye-tracking and structured data analysis can reveal critical insights into learning and assessment behaviors.
  • This approach holds significant potential for improving psychometric modeling and educational instrument design.