Application of Dynamic Process Neural Network Model Identification in Ethnic Dance Online Teaching System

Area of Science:

• Education Technology
• Artificial Intelligence
• Dance Pedagogy

Background:

• Traditional dance classroom teaching struggles to adapt to modern educational trends.
• Existing university dance education methods require innovation to meet contemporary demands.

Purpose of the Study:

• To propose an online distance teaching model for folk dance using modern information technology.
• To develop a new teaching paradigm for folk dance education adaptable to the digital age.

Main Methods:

• Analysis of current challenges in university dance education.
• Application of artificial intelligence and dynamic process neural network model identification.
• Utilizing time series data mining for classification and prediction.

Main Results:

• Development of a dynamic process neural network model overcoming traditional input limitations.
• Demonstration of AI's capability in analyzing time series data for dance education contexts.
• Establishment of a flexible online remote network for ethnic dance teaching.

Conclusions:

• Online distance education offers a flexible and innovative model for folk dance courses.
• The dynamic process neural network model provides an advanced approach for data analysis in educational technology.
• Integrating AI and online platforms can revolutionize ethnic dance teaching and learning.