Development of Spectral Clustering Algorithm in Cognitive Diagnosis Model: Approach for Student’s Psychological Growth

With the development of intelligent education, the diagnostic performance of the traditional cognitive diagnostic model has been unable to meet the needs of today’s education. This study uses the Gaussian mixture model (GMM) to model and optimize model parameters through maximum probability estimati...

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Vydáno v:International journal of computational intelligence systems Ročník 18; číslo 1; s. 211 - 15
Hlavní autor: Chang, Xiao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 18.08.2025
Springer Nature B.V
Springer
Témata:
Algorithms
Artificial Intelligence
Cluster analysis
Clustering
Cognition & reasoning
Cognitive ability
Cognitive diagnosis
Computational Intelligence
Control
Datasets
Diagnosis
Education
Engineering
Gaussian mixture model
Graph segmentation
Information technology
Mathematical Logic and Foundations
Mechatronics
Methods
Optimization
Probabilistic models
Psychological development
Research Article
Robotics
Spectral clustering algorithm
Students
Psychological development
Spectral clustering algorithm
Cognitive diagnosis
Gaussian mixture model
Graph segmentation
ISSN:1875-6883, 1875-6891, 1875-6883
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Shrnutí:With the development of intelligent education, the diagnostic performance of the traditional cognitive diagnostic model has been unable to meet the needs of today’s education. This study uses the Gaussian mixture model (GMM) to model and optimize model parameters through maximum probability estimation. The spectral clustering (SC) algorithm iterative optimization was combined with a similarity matrix and Laplacian matrix to construct an improved spectral clustering cognitive diagnosis model. The proposed SC algorithm’s clustering accuracy was 0.95, ARI was 0.86, and FMI was 0.85, and its clustering performance was better than that of other comparison algorithms. The cognitive diagnosis model based on the SC algorithm showed 4.01 SC, and the psychological status score was 3.97. The clustering performance of the model proposed in this study showed a favorable outcome. Moreover, the cognitive diagnosis model based on SC can meet the cognitive diagnosis needs of most students and help improve their cognitive ability. The enhanced cognitive diagnostic model combining SC and the GMM proposed has significant advantages in clustering performance and educational application effects, providing technical support for promoting students’ psychological growth.
Bibliografie:ObjectType-Article-1
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ISSN:1875-6883
1875-6891
1875-6883
DOI:10.1007/s44196-025-00849-w