Research on personalized teaching modeling based on dynamic entropy control clustering: Innovation and application of AKM-DWEC framework

The growing demand for personalized education necessitates intelligent clustering methods capable of handling dynamic, high-dimensional educational data while preserving privacy. This study proposes an Adaptive K-means framework with Dynamic Weight Entropy Control (AKM-DWEC), which addresses three c...

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Veröffentlicht in:	Systems and soft computing Jg. 7; S. 200353
1. Verfasser:	Chen, Xihua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.12.2025 Elsevier
Schlagworte:	Adaptive clustering algorithm Differential privacy preservation Dynamic weight entropy control Multimodal data governance Personalized learning model Multimodal data governance Differential privacy preservation Personalized learning model Adaptive clustering algorithm Dynamic weight entropy control
ISSN:	2772-9419, 2772-9419
Online-Zugang:	Volltext
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