Modeling Cross-Cultural Competence in Vocational Education Internationalization Using Neutrosophic SuperHyperFunctions and Big Data-Driven Cultural Clusters

In the global landscape of vocational education, cross-cultural competence is vital for successful international collaboration and student engagement. However, most traditional frameworks are too rigid to capture the evolving, uncertain, and multi-layered nature of cultural identities. This paper in...

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Main Authors: Min Huang, Liang Zhang, Fenghua Li
Format: Article
Language:English
Published: University of New Mexico 2025-07-01
Series:Neutrosophic Sets and Systems
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Online Access:https://fs.unm.edu/NSS/25CrossCultural.pdf
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Summary:In the global landscape of vocational education, cross-cultural competence is vital for successful international collaboration and student engagement. However, most traditional frameworks are too rigid to capture the evolving, uncertain, and multi-layered nature of cultural identities. This paper introduces a novel approach that models crosscultural competence using Neutrosophic SuperHyperFunctions. These functions allow for the representation of dynamic cultural clusters extracted from big data sources, including digital learning platforms and international student interactions. By structuring culture as a system of nested and sometimes contradictory traits, we use nth-order powersets and neutrosophic logic to define competence across individual, group, institutional, and national levels. A comprehensive mathematical framework is established, including formal definitions, axioms, and symbolic functions. Realistic, fully calculated case studies demonstrate how this model captures the uncertainty and fluidity of real-world crosscultural experiences. The proposed method offers an adaptive, data-integrated pathway to support decision-making in curriculum design, teacher training, and policy formation in vocational education internationalization.
ISSN:2331-6055
2331-608X