Abstract: This study addresses the need for data analysis in evaluating the teaching outcomes of higher music education. It proposes a solution using data-driven algorithms to measure and analyze these outcomes. This study focuses on the issue of measuring and evaluating the outcomes of music education teaching. It analyzes the process of measuring and assessing these outcomes, designs a program for doing so, and introduces key technologies such as music education teaching process analysis, measurement of music teaching outcomes, construction of an assessment model for music teaching outcomes, and application of the assessment model. The study selects teaching content, practical skills, and social practice ability as the three aspects to evaluate. The results demonstrate that this method achieves higher assessment accuracy and requires less time, effectively addressing the challenge of measuring and evaluating the teaching outcomes of higher music education using big data. The findings demonstrate that the technique exhibits a high level of assessment accuracy and is less time-consuming. Additionally, it effectively addresses the challenge of measuring and evaluating the teaching accomplishments in higher music education from the viewpoint of big data.

Yifan Fan, “Data Mining MRO-BP Network-Based Evaluation Effectiveness of Music Teaching” International Journal of Advanced Computer Science and Applications(IJACSA), 16(1), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160118

@article{Fan2025,
title = {Data Mining MRO-BP Network-Based Evaluation Effectiveness of Music Teaching},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160118},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160118},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {1},
author = {Yifan Fan}
}