Research on personalized teaching modeling based on dynamic entropy control clustering: Innovation and application of AKM-DWEC framework - Chen, Xihua (Systems and soft computing, 2025)
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.