Spectral Clustering Algorithm for Cognitive Diagnostic Assessment

Frontiers in Psychology

article

Lei Guo¹  Jing Yang³  Naiqing Song² 

[1] Faculty of Psychology, Southwest University;Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality;School of Mathematics and Statistics, Northeast Normal University;Basic Education Research Center, Southwest University;Urban and Rural Education Research Center, Southwest University

关键词: cognitive diagnostic assessment;    spectral clustering;    K-means;    G-DINA model;    classification accuracy;   

DOI  :  10.3389/fpsyg.2020.00944

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

In cognitive diagnostic assessment (CDA), clustering analysis is an efficient approach to classify examinees into attribute-homogeneous groups. Many researchers have proposed different methods, such as the nonparametric method with Hamming distance, K-means method, and hierarchical agglomerative cluster analysis, to achieve the classification goal. In this paper, according to their responses, we introduce a spectral clustering algorithm (SCA) to cluster examinees. Simulation studies are used to compare the classification accuracy of the SCA, K-means algorithm, G-DINA model and its related reduced cognitive diagnostic models. A real data analysis is also conducted to evaluate the feasibility of the SCA. Some research directions are discussed in the final section.

【 授权许可】

CC BY