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Related Experiment Video

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Corticospinal Excitability Modulation During Action Observation
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Joint modeling of action sequences and action time in computer-based interactive tasks.

Yanbin Fu1, Peida Zhan2,3, Qipeng Chen1

  • 1School of Psychology, Zhejiang Normal University, Jinhua, China.

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|July 10, 2023
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Summary
This summary is machine-generated.

This study introduces an action-level joint model to analyze process data from computer-based assessments. Incorporating action time improves understanding of participant problem-solving behaviors.

Keywords:
Action sequenceAction timeItem response theoryJoint modelingProcess data

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Cognitive Psychology

Background:

  • Process data in computer-based assessments offers insights into problem-solving.
  • Action time is a crucial component of process data, reflecting task completion speed.
  • Existing models often analyze action sequences or item responses separately.

Purpose of the Study:

  • To propose an action-level joint model for analyzing process data, integrating action sequences and action time.
  • To extend the sequential response model (SRM) and conventional item-level joint models.
  • To provide a latent variable modeling framework for deeper analysis of behavioral patterns.

Main Methods:

  • Developed an action-level joint model combining a sequential response model (SRM) for action sequences.
  • Proposed a novel log-normal model for measuring action time.
  • Integrated action time into a joint-hierarchical modeling framework.

Main Results:

  • Empirical and simulation studies validated the model's justification and parameter interpretability.
  • Parameter estimates demonstrated accuracy.
  • Including action time enhanced the understanding of participants' behavioral patterns.

Conclusions:

  • The proposed action-level joint model offers an innovative framework for process data analysis in computer-based assessments.
  • The model effectively integrates action sequences and action time for a comprehensive view of problem-solving.
  • This approach advances latent variable modeling in educational and psychological research.