ArticleFrontiers in psychology2020
Spectral Clustering Algorithm for Cognitive Diagnostic Assessment.
Article in Frontiers in psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Fine-grained evaluation of large language models in medicine using non-parametric cognitive diagnostic modeling.Scientific reports · 2026Article
- The influence of social interactions in mitigating psychological distress during the COVID-19 pandemic: a study in Sri Lanka.Frontiers in psychology · 2024Article
- Test Assembly for Cognitive Diagnosis Using Mixed-Integer Linear Programming.Frontiers in psychology · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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