Q-Matrix Designs of Longitudinal Diagnostic Classification Models With Hierarchical Attributes for Formative Assessment

• Main Authors: Wei Tian, Jiahui Zhang, Qian Peng, Xiaoguang Yang
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-07-01
• Series: Frontiers in Psychology
• Subjects: Q-matrix; longitudinal DCMs; hierarchical attributes; TDCM; HDCM
• Online Access: https://www.frontiersin.org/article/10.3389/fpsyg.2020.01694/full

Longitudinal diagnostic classification models (DCMs) with hierarchical attributes can characterize learning trajectories in terms of the transition between attribute profiles for formative assessment. A longitudinal DCM for hierarchical attributes was proposed by imposing model constraints on the transition DCM. To facilitate the applications of longitudinal DCMs, this paper explored the critical topic of the Q-matrix design with a simulation study. The results suggest that including the transpose of the R-matrix in the Q-matrix improved the classification accuracy. Moreover, 10-item tests measuring three linear attributes across three time points provided satisfactory classification accuracy for low-stakes assessment; lower classification rates were observed with independent or divergent attributes. Q-matrix design recommendations were provided for the short-test situation. Implications and future directions were discussed.