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Improved Student Modeling and Data Clustering for Personalized Online Teaching of International Chinese Teachers: The MKmeans Algorithm Approach

Year: 2025

Type: article

Abstract: This study explores personalized online teaching for international Chinese education by enhancing student modeling through advanced data mining. A novel MKmeans clustering algorithm, based on mean shift theory, is proposed to improve clustering stability and noise resilience. Experiments on datasets like Iris and Wine demonstrate MKmeans outperforms classical Kmeans in accuracy and F-measure. The findings enable adaptive teaching strategies, offering significant insights for optimizing online learning systems and advancing personalized education.

Source: Journal of Information Systems Engineering & Management

DOI: 10.52783/jisem.v10i16s.2582

Author: Jing Zhao

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: diamond

OpenAlex ID: W4408242684

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