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This study introduces a new cognitive diagnostic model (CDM) that accounts for rapid guessing (RG) in test-takers. The enhanced model improves accuracy in assessing student skills and personalizing feedback by incorporating response times.

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DINA modelDINO modelG-DINA modelrapid guessingresponse time

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Learning Analytics

Background:

  • Many students engage in rapid guessing (RG), answering questions quickly without careful thought.
  • Existing cognitive diagnostic models (CDMs) do not account for RG, potentially limiting personalized feedback accuracy.
  • Assessing fine-grained skills requires models that can differentiate between genuine understanding and guessing strategies.

Purpose of the Study:

  • To propose and evaluate an advanced CDM that incorporates RG by utilizing both item response and response time data.
  • To enhance the accuracy and precision of skill assessment and feedback personalization in educational testing.
  • To investigate the impact of modeling RG on the performance of CDMs compared to traditional models.

Main Methods:

  • Developed a novel CDM integrating item response theory and response time data to model RG.
  • Employed Markov chain Monte Carlo (MCMC) methods within JAGS for parameter recovery simulations.
  • Applied both standard and the proposed CDMs to analyze student response data from the PISA 2015 mathematics assessment.

Main Results:

  • The proposed CDM demonstrated superior performance over a standard CDM that ignored RG.
  • The new model exhibited reduced bias and increased precision in estimating item and person parameters.
  • Classification accuracy of student skill mastery was significantly improved with the RG-informed CDM.
  • Empirical analysis revealed varying levels of RG behavior across different test items.

Conclusions:

  • Modeling rapid guessing significantly enhances the accuracy and reliability of cognitive diagnostic models.
  • The proposed advanced CDM offers a more precise and effective tool for personalized learning and feedback.
  • Findings underscore the importance of considering response time and guessing behaviors in educational assessments.