Data-Driven Educational Intelligence for Cultural Heritage Training: A Blended Learning Framework for Minority Dance Education

Dan Zhou; Thosporn Sangsawang

doi:10.37497/eagleSustainable.v16i.630

Autores

Dan Zhou Vocational Educational Division, Faculty of Technical Education, Rajamangala University of Technology Thanyaburi, Pathum Thani, Thailand. https://orcid.org/0009-0002-5118-7947
Thosporn Sangsawang Vocational Educational Division, Faculty of Technical Education, Rajamangala University of Technology Thanyaburi, Pathum Thani, Thailand. https://orcid.org/0000-0002-7926-6949

DOI:

https://doi.org/10.37497/eagleSustainable.v16i.630

Palavras-chave:

Aprendizagem Híbrida Digital, Inteligência Educacional Cultural, Educação em Dança de Minorias, Análise Fatorial Exploratória, Análise de Cluster, Análise de Dados Educacionais

Resumo

Objetivo: As tradições de dança de minorias representam formas significativas de patrimônio cultural que são preservadas por meio da transmissão educacional. No entanto, sua sustentabilidade é cada vez mais desafiada pela escassez de instrutores especializados, barreiras geográficas e mudanças nas preferências dos aprendizes em ambientes digitais de aprendizagem. Em resposta a esses desafios, este estudo propõe um framework de aprendizagem híbrida orientado por dados para a educação em dança de minorias, integrando o ensino tradicional com plataformas digitais e gerando insights analíticos para apoiar a inteligência educacional e a tomada de decisão institucional.

Metodologia/Abordagem: O framework incorpora tecnologia de captura de movimento com um Autoencoder Variacional Condicional otimizado por Artificial Gorilla Troop (AGTO-CVAE) para permitir a análise e reconhecimento precisos dos movimentos de dança dos estudantes. Os dados foram coletados de alunos matriculados em programas de dança de minorias por meio de questionários estruturados, avaliações de desempenho e métricas de engajamento online. As sequências de captura de movimento foram normalizadas antes da análise. A Análise Fatorial Exploratória (AFE) foi aplicada para identificar dimensões latentes do ambiente de aprendizagem híbrida, e o algoritmo K-means foi utilizado para agrupar os aprendizes conforme adaptabilidade tecnológica, engajamento e orientação cultural.

Originalidade/Relevância: O framework integra o ensino tradicional com plataformas digitais, ao mesmo tempo em que gera insights analíticos para apoiar a inteligência educacional e a tomada de decisão institucional no contexto da educação em dança de minorias.

Principais Resultados: Os resultados experimentais demonstram que o modelo AGTO-CVAE proposto apresenta desempenho superior, com precisão de 0,9875, recall de 0,9845 e F1-score de 0,9859, além de reduzir a complexidade computacional.

Contribuições Teóricas/Metodológicas: Os achados indicam que ambientes de aprendizagem híbrida orientados por dados podem funcionar como sistemas de inteligência educacional, promovendo melhorias no design instrucional, no engajamento dos estudantes e na sustentabilidade da educação do patrimônio cultural.

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Biografia do Autor

Dan Zhou, Vocational Educational Division, Faculty of Technical Education, Rajamangala University of Technology Thanyaburi, Pathum Thani, Thailand.

Vocational Educational Division, Faculty of Technical Education, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand.

Thosporn Sangsawang, Vocational Educational Division, Faculty of Technical Education, Rajamangala University of Technology Thanyaburi, Pathum Thani, Thailand.

Educational Technology and Communications Division, Faculty of Technical Education, Rajamangala University of Technology Thanyaburi, Pathum Thani, 12110, Thailand

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