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An Improved Parameter-Estimating Method in Bayesian Networks Applied for Cognitive Diagnosis Assessment.

Ling Ling Wang1,2, Tao Xin3, Liu Yanlou4

  • 1School of Educational Science, Shenyang Normal University, Shenyang, China.

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|June 10, 2021
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Summary

This study introduces a new method for cognitive diagnostic assessment using Bayesian networks (BNs). It improves parameter estimation, enhancing classification accuracy and outperforming existing models in certain scenarios.

Keywords:
Bayesian Networkscognitive diagnostic assessmentcognitive diagnostic modelideal response patternparameter estimating method

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Area of Science:

  • Educational Measurement
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Bayesian networks (BNs) are increasingly used for cognitive diagnostic assessment (CDA).
  • Current methods often rely on Markov Chain Monte Carlo (MCMC) for parameter estimation.
  • Existing algorithms like Expectation-Maximization (EM) and Gradient Descent (GD) face challenges with monotonic constraints in educational assessment.

Purpose of the Study:

  • To propose a novel parameter estimation method for BNs in CDA.
  • To address the limitations of EM and GD algorithms regarding monotonic constraints.
  • To enhance the statistical classification performance of BNs for CDA.

Main Methods:

  • Train Bayesian networks (BNs) using ideal response pattern (IRP) data.
  • Estimate BN parameters using EM or Gradient Descent (GD) with IRP-derived informative priors.
  • Validate the method through simulation studies and real-world data analysis.

Main Results:

  • The proposed method effectively estimates BN parameters while respecting monotonic constraints.
  • Simulation and real data analyses confirm the validity and feasibility of the approach.
  • Bayesian networks trained with the new method show improved statistical classification performance.

Conclusions:

  • The novel parameter estimation technique enhances the application of BNs in cognitive diagnostic assessment.
  • This method offers a viable alternative to traditional approaches, particularly when monotonic constraints are crucial.
  • The proposed BN approach demonstrates competitive or superior performance compared to models like G-DINA.