Big Data-Driven Optimization Model for Art Education Resource Allocation in China

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An optimization model improves resource allocation in art education, suggesting big data enhances decision-making.

The big data-driven optimization model significantly reduced teacher-student ratio disparity by 44.8%.
Digital equipment coverage in under-resourced areas increased by 261.1% using the new resource allocation approach.
The methods included clustering, association rule mining, and regression applied in three case studies.
This model provides a replicable framework for achieving more equitable distribution of art education resources.

Anming Zhang (2025) studied this question.

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