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A state response measurement model for problem-solving process data.

Yue Xiao1,2, Hongyun Liu3,4

  • 1Department of Educational Psychology, Faculty of Education, East China Normal University, Shanghai, China.

Behavior Research Methods
|January 3, 2023
PubMed
Summary
This summary is machine-generated.

The new State Response (SR) model better analyzes interactive task sequences than existing models. It accurately estimates parameters, especially when task difficulty varies within steps, improving upon the DC model.

Keywords:
Measurement modelingProblem-solvingProcess dataState response model

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Cognitive Modeling

Background:

  • Interactive tasks generate action sequences revealing respondent and task information.
  • Existing models like the DC model have limitations in capturing task process characteristics.

Purpose of the Study:

  • To propose a State Response (SR) measurement model using a Bayesian approach for analyzing process sequences.
  • To compare the SR model's performance against the DC model.

Main Methods:

  • Developed a Bayesian State Response (SR) measurement model.
  • Assumed actions are determined by problem-solving ability and current state easiness.
  • Utilized simulation studies and empirical data from the Program for International Student Assessment 2012.

Main Results:

  • The SR model demonstrated strong performance in parameter estimation.
  • SR model accuracy was comparable to the DC model when state easiness was uniform, but superior when it varied.
  • The SR model showed a better fit for empirical data and provided meaningful state easiness estimates.

Conclusions:

  • The proposed SR model offers a more realistic and accurate approach to analyzing interactive task data.
  • The SR model effectively captures within-task variations in state easiness, enhancing understanding of task steps.