Various item selection algorithms have been proposed for cognitive diagnostic computerized adaptive testing (CD-CAT), with the goal of efficiently diagnosing examinees’ strengths and weaknesses. However, these algorithms often come with significant computational costs, which can hinder their practical implementation. A likelihood-based profile shrinkage (LBPS) algorithm is proposed to simplify the item selection process and reduce the computational cost in CD-CAT. Our simulation results indicate that incorporating LBPS into existing item selection methods yields substantial computational efficiency gains, with greater reductions in computation time as the number of attributes and test length increase. Additionally, LBPS maintains estimation accuracy at both the attribute and pattern levels. These findings suggest that LBPS is a scalable and effective solution for the item selection of CD-CAT in complex scenarios.
