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

Frontiers in Psychology

article

Wei Tian¹  Jiahui Zhang¹  Xiaoguang Yang¹ 

[1] Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University

关键词: Q-matrix;    longitudinal DCMs;    hierarchical attributes;    TDCM;    HDCM;   

DOI  :  10.3389/fpsyg.2020.01694

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY