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This study introduces a new method to analyze process data from computer-based assessments (CBAs). The approach offers detailed insights into problem-solving skills and abilities, enhancing diagnostic classification.

Keywords:
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Area of Science:

  • Educational Measurement
  • Cognitive Psychology
  • Data Science

Background:

  • Process data from computer-based assessments (CBAs) offer rich insights into problem-solving behaviors.
  • Traditional outcome data provide limited understanding of how individuals solve problems.
  • Diagnostic classification models are valuable for understanding cognitive skills.

Purpose of the Study:

  • To propose an item expansion method for analyzing action-level process data.
  • To apply diagnostic classification to understand respondents' problem-solving competence.
  • To evaluate the method's effectiveness using a Programme for International Student Assessment (PISA) item.

Main Methods:

  • Developed an item expansion method for analyzing action-level process data.
  • Employed diagnostic classification to estimate latent classes and abilities.
  • Utilized a PISA problem-solving item for empirical illustration.

Main Results:

  • Estimated latent classes provided more detailed diagnoses of problem-solving skills than observed scores.
  • Higher-order latent ability estimation was more accurate than unidimensional estimation from outcome data.
  • Problem-solving skill interactions followed a conjunctive condensation rule, indicating mastery of all required skills for specific action sequences.

Conclusions:

  • The proposed diagnostic classification approach is a feasible and promising method for analyzing process data.
  • This method enhances the understanding of respondents' problem-solving competence beyond traditional outcome measures.
  • The findings highlight the value of process data in educational measurement and cognitive diagnostics.