Palabras clave
Cultural Education Intelligence
Minority Dance Education
Exploratory Factor Analysis
Cluster Analysis
Educational Data Analytics
Cómo citar
Resumen
Purpose: Minority dance traditions represent significant forms of cultural heritage that are preserved through educational transmission. However, their sustainability is increasingly challenged by limited expert instructors, geographic barriers, and evolving learner preferences in digital learning environments. In response to these challenges, this study proposes a data-driven blended learning framework for minority dance education that integrates traditional instruction with digital platforms while generating analytical insights to support educational intelligence and institutional decision-making.
Methodology/approach: The framework incorporates motion capture technology with an Artificial Gorilla Troop Optimized Conditional Variational Autoencoder (AGTO-CVAE) to enable accurate analysis and recognition of students’ dance movements. Data were collected from students enrolled in minority dance programs through structured questionnaires, performance evaluations, and online engagement metrics. Motion capture sequences were normalized prior to analysis. Exploratory Factor Analysis (EFA) was applied to identify latent dimensions of the blended learning environment, and K-means clustering was used to group learners according to technological adaptability, engagement, and cultural orientation.
Originality/Relevance: The framework integrates traditional instruction with digital platforms while generating analytical insights to support educational intelligence and institutional decision-making in minority dance education.
Key findings: Experimental results show that the proposed AGTO-CVAE model achieves superior performance, with precision of 0.9875, recall of 0.9845, and an F1-score of 0.9859, while reducing computational complexity.
Theoretical/methodological contributions: The findings demonstrate that data-driven blended learning environments can function as educational intelligence systems, supporting improved instructional design, learner engagement, and sustainable cultural heritage education.
Citas
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