Blended Learning Model for Enhancing Minority Dance Education Validated through Exploratory Factor and Cluster Analyses

This study proposes a blended learning model for minority dance education that integrates traditional face-to-face instruction with digital learning resources and AI-based motion analysis. Using a dataset of 19,385 learner records, the research applies Z-score normalization, Exploratory Factor Analysis (EFA), and K-means clustering to identify learner engagement, performance, and cultural awareness factors. The proposed AGTO-CVAE model analyzes dance movements and demonstrates high performance with precision (0.9875), recall (0.9845), and F1-score (0.9859). The findings indicate that the blended learning approach enhances technical dance skills, cultural understanding, and learner engagement.