Data-Driven Learning of Q-Matrix.
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-knownQ-matrix, which specifies the item-attribute relationships. This article proposes a data-driven approach to identification of theQ-matrix and estimation of related model parameters. A key ingredient is a flexibleT-matrix that relates theQ-matrix to response patterns. The flexibility of theT-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 theQ-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.
Columbia University, New York, USA.
Jingchen
J
Liu
Gongjun
G
Xu
Zhiliang
Z
Ying
DINA model; cognitive diagnosis; latent traits; model selection; multidimensionality; optimization; self-learning; statistical estimation