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A Higher-Order Cognitive Diagnosis Model with Ordinal Attributes for Dichotomous Response Data
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DOI：10.1080/00273171.2020.1860731.png)
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Abstract
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Most existing cognitive diagnosis models (CDMs) assume attributes are binary latent variables, which may be oversimplified in practice. This article introduces a higher-order CDM with ordinal attributes for dichotomous response data. The proposed model can either incorporate domain experts' knowledge or learn from the data empirically by regularizing model parameters. A sequential item response model was employed for joint attribute distribution to accommodate the sequential mastery mechanism. The expectation-maximization algorithm was employed for model estimation, and a simulation study was conducted to assess the recovery of model parameters. A set of real data was also analyzed to assess the viability of the proposed model in practice.
Keywords:
Cognitive diagnosis
regularization
polytomous attribute
higher-order
sequential IRT
lasso
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