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Summary

Dynamic Bayesian networks (DBNs) show promise for complex student assessment modeling. Good measurement quality is key for accurate parameter recovery, even with small sample sizes like 400.

Keywords:
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Area of Science:

  • Educational measurement
  • Psychometric modeling
  • Statistical modeling

Background:

  • Dynamic Bayesian networks (DBNs) offer a powerful framework for modeling complex student proficiency in rich measurement scenarios.
  • However, DBNs are understudied, with limited knowledge regarding their psychometric properties and performance under realistic conditions.

Purpose of the Study:

  • To explore the psychometric properties of Dynamic Bayesian networks (DBNs).
  • To evaluate parameter recovery for DBNs under various realistic psychometric conditions using Monte Carlo simulations.

Main Methods:

  • A Monte Carlo simulation study was employed to assess parameter recovery in DBNs.
  • Key factors manipulated included sample size, measurement quality, test length, and the number of measurement occasions.

Main Results:

  • Measurement quality emerged as the most significant factor influencing estimation quality, with more distinct performance categories improving estimation.
  • Sufficient parameter recovery was observed with sample sizes as low as N = 400, provided measurement quality was adequate and each occasion had at least three items.

Conclusions:

  • DBNs are viable for complex assessment modeling, with parameter recovery being robust under specific conditions.
  • Even single-item tests can yield adequate parameter recovery if measurement quality is exceptionally high.