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A General Mixture Model for Cognitive Diagnosis

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  • Joemari Olea
  • Kevin Carl Santos

    (University of the Philippines Diliman)

Abstract

Although the generalized deterministic inputs, noisy “and†gate model (G-DINA; de la Torre, 2011) is a general cognitive diagnosis model (CDM), it does not account for the heterogeneity that is rooted from the existing latent groups in the population of examinees. To address this, this study proposes the mixture G-DINA model, a CDM that incorporates the G-DINA model within the finite mixture modeling framework. An expectation–maximization algorithm is developed to estimate the mixture G-DINA model. To determine the viability of the proposed model, an extensive simulation study is conducted to examine the parameter recovery performance, model fit, and correct classification rates. Responses to a reading comprehension assessment were analyzed to further demonstrate the capability of the proposed model.

Suggested Citation

  • Joemari Olea & Kevin Carl Santos, 2024. "A General Mixture Model for Cognitive Diagnosis," Journal of Educational and Behavioral Statistics, , vol. 49(2), pages 268-307, April.
    • Handle: RePEc:sae:jedbes:v:49:y:2024:i:2:p:268-307
    DOI: 10.3102/10769986231176012
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    References listed on IDEAS

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