Leveraging Digital Interactive Frameworks to Enhance Dance Education using PLS-SEM and IPMA

This study investigates the intermediary role of knowledge transfer (KT) in the relationship between digital interaction and deep learning (DL) within dance education. The study identifies gaps in how digital platforms support KT by leveraging a robust sample of 261 participants and employing Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance-Performance Map Analysis (IPMA). The findings emphasize the critical mediating function of KT and highlight the necessity of enhancing interactions among students and students (SS), students and teachers (ST), and students and content (SC) in digital settings to foster DL. Given the pivotal role of KT, the importance of ST and SS interactions is further analyzed through IPMA. This study proposes to refine digital learning experiences in dance education, targeted strategies are proposed, such as incorporating interactive digital resources, promoting collaborative learning among students, and intensifying teacher-student engagement. Ultimately, by facilitating effective KT, this study offers meaningful insights and actionable recommendations for achieving deeper learning outcomes and improving digital learning environments in dance education.