Application of Fuzzy Clustering Algorithm in the Analysis of Students’ Learning Styles on International Education Platforms

Authors

  • Yanling Hu Henan Open University, Zhengzhou 450046, Henan, China

Abstract

This paper discusses the analysis and application of middle school students’ learning styles in a cross-border education platform, facilitated by a fuzzy clustering algorithm. With the increasing popularity of online education, it has become important to personalize the course content and teaching approach according to students’ individual learning styles. How to do so in order to improve learning outcomes has become a core issue in the education field. For this study, student learning data from multiple cross-border education platforms was collected, and a fuzzy clustering algorithm was applied to classify students’ learning behaviors. “Five main learning styles were identified: visual, auditory, kinesthetic, reading/writing, and multimodal , in which learners flexibly combine multiple strategies rather than relying on a single mode. These learning styles reflect the differences
in students’ preferences during the learning process, demonstrating the importance of designing personalized learning paths. The research results show that the fuzzy clustering algorithm can cope with the diversity and complexity of students’ learning behavior, accurately identify each student’s learning style by calculating the degree of membership, and provide a basis for a personalized teaching strategy design for the platform. The evaluation results demonstrate that the proposed fuzzy clustering algorithm enhances not only the classification accuracy but also the robustness of learning style identification, ensuring consistent and reliable outcomes under varying data conditions. This study provides new ideas and methods whereby cross-border education platforms can analyze students’ personalized learning, and provides theoretical basis and practical guidance for educators and platform developers wishing to implement personalized teaching.

Keywords: fuzzy clustering algorithm; cross-border education; learning style; personalized teaching; data analysis

Cite As

Y. Hu, "Application of Fuzzy Clustering Algorithm in the Analysis of Students’ Learning Styles on
International Education Platforms", Engineering Intelligent Systems, vol. 33 no. 6, pp. 711-719, 2025.

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Published

2025-11-01

Issue

Vol. 33 No. 6 (2025): International Journal of Engineering Intelligent Systems

Section

Article

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