Intelligent Music Education System: Utilizing Algorithms for Personalized Learning Experience
Abstract
The integration of intelligent technologies in music education heralds a new era, facilitating personalized learning experiences. This paper explores innovative approaches to enhance music education through algorithmic solutions. The aim of the study is to develop an intelligent Music Education System (MES) that harnesses algorithms to create personalized learning paths for students. A dataset comprising students’ online music learning behaviors, capturing various aspects of their interaction with music education platforms is utilized. Data pre-processing involves standardization and normalization techniques to ensure consistency. The Intelligent Generative Adversarial Network (IGAN) is employed for the automatic generation of music accompaniment, while a Redefined Adam Optimization (RAO) algorithm is utilized to optimize the recommendation process for personalized learning paths. This system incorporates intelligent algorithms to provide adaptive learning experiences tailored to individual student needs, fostering improved engagement and outcomes. Preliminary results indicate significant enhancements in student performance with a marked increase in final course grades compared to traditional methods. The proposed RAO-GAN strategy fared better than the conventional approaches, with student satisfaction ratings of 94.5%, skill advancement scores of 94%, and emotional expression ability scores of 93%. In personalized music learning, the proposed RAO-IGAN strategy showed remarkably consistent performance across the main parameters, with precision, 1-Score, recall and accuracy of 0.99, 0.98, 0.98 and 0.99. The established intelligent MES successfully merges music education with advanced algorithms, offering a framework for cultivating innovative musical talents through personalized learning experiences.
Remember to check out the Most Cited Articles! |
---|
Check out these Notable Titles in Antennas |