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 |
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01.12.2025
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| Abstract | 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 critical challenges in educational data analysis: (1) Dynamic feature relevance through entropy-regularized weight optimization, explicitly constraining weight uniformity with a novel regularization term λ∑i=1d(Wi−1d)2; (2) Real-time adaptation via incremental micro-cluster updating, reducing computational overhead by 85.3 %; and (3) privacy-utility balance achieved through dual perturbation combining Laplace noise (ε1=0.4) and Gaussian random projection (ε2=0.6). Experimental validation on 800 students' multimodal dataset demonstrates superior performance with 0.892 silhouette coefficient (25.1 % improvement over baseline K-means) and 91.3 % F1-score in learner categorization, while maintaining 91.2 % clustering purity under (ε=1.0, δ=10^5) - differential privacy. The framework's educational impact is evidenced by 28.7 % knowledge mastery improvement (p < 0.001) and 41.7 % reduction in recommendation errors. This work advances personalized education through its dynamic adaptation mechanism and rigorous privacy preservation, though cross-cultural applicability requires further investigation. |
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| AbstractList | 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 critical challenges in educational data analysis: (1) Dynamic feature relevance through entropy-regularized weight optimization, explicitly constraining weight uniformity with a novel regularization term λ∑i=1d(Wi−1d)2; (2) Real-time adaptation via incremental micro-cluster updating, reducing computational overhead by 85.3 %; and (3) privacy-utility balance achieved through dual perturbation combining Laplace noise (ε1=0.4) and Gaussian random projection (ε2=0.6). Experimental validation on 800 students' multimodal dataset demonstrates superior performance with 0.892 silhouette coefficient (25.1 % improvement over baseline K-means) and 91.3 % F1-score in learner categorization, while maintaining 91.2 % clustering purity under (ε=1.0, δ=10^5) - differential privacy. The framework's educational impact is evidenced by 28.7 % knowledge mastery improvement (p < 0.001) and 41.7 % reduction in recommendation errors. This work advances personalized education through its dynamic adaptation mechanism and rigorous privacy preservation, though cross-cultural applicability requires further investigation. |
| ArticleNumber | 200353 |
| Author | Chen, Xihua |
| Author_xml | – sequence: 1 givenname: Xihua orcidid: 0000-0002-5855-2225 surname: Chen fullname: Chen, Xihua email: qxihua@126.com organization: School of Engineering, Guangzhou College of Technology and Business, Guangzhou 510850, Guangdong, PR China |
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| Cites_doi | 10.1007/s40747-022-00647-3 10.1007/978-3-031-80943-9_89 10.1109/TKDE.2023.3297226 10.3991/ijim.v18i06.46289 10.1016/j.heliyon.2024.e31244 10.1109/TLT.2024.3464560 10.3390/app13053267 |
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| Keywords | Multimodal data governance Differential privacy preservation Personalized learning model Adaptive clustering algorithm Dynamic weight entropy control |
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Applic. – year: 2024 ident: 10.1016/j.sasc.2025.200353_bib0031 article-title: Real-time clustering-intervention pipeline for adaptive learning publication-title: IEEE TLT – year: 2024 ident: 10.1016/j.sasc.2025.200353_bib0019 article-title: Cross-Cultural adaptation of clustering models in MOOCs publication-title: AIED – year: 2024 ident: 10.1016/j.sasc.2025.200353_bib0029 article-title: Causal invariant clustering for education equity analysis publication-title: AAAI – year: 2023 ident: 10.1016/j.sasc.2025.200353_bib0021 article-title: Curriculum contrastive deep clustering publication-title: ICML – start-page: 1880 year: 2024 ident: 10.1016/j.sasc.2025.200353_bib0025 article-title: Capacitated online clustering algorithm. ECAI 2024:27th European Conference on Artificial Intelligence |
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| SubjectTerms | Adaptive clustering algorithm Differential privacy preservation Dynamic weight entropy control Multimodal data governance Personalized learning model |
| Title | Research on personalized teaching modeling based on dynamic entropy control clustering: Innovation and application of AKM-DWEC framework |
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