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- W1993753443 abstract "The recent surge of interests in cognitive assessment has led to developments of novel statistical models for diagnostic classification. Central to many such models is the well-known Q-matrix, which specifies the item–attribute relationships. This article proposes a data-driven approach to identification of the Q-matrix and estimation of related model parameters. A key ingredient is a flexible T-matrix that relates the Q-matrix to response patterns. The flexibility of the T-matrix allows the construction of a natural criterion function as well as a computationally amenable algorithm. Simulations results are presented to demonstrate usefulness and applicability of the proposed method. Extension to handling of the Q-matrix with partial information is presented. The proposed method also provides a platform on which important statistical issues, such as hypothesis testing and model selection, may be formally addressed." @default.
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- W1993753443 date "2012-08-16" @default.
- W1993753443 modified "2023-10-16" @default.
- W1993753443 title "Data-Driven Learning of Q-Matrix" @default.
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- W1993753443 doi "https://doi.org/10.1177/0146621612456591" @default.
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