<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The equitable distribution of educational resources epitomizes the essence of establishing a robust educational nation. Yet, the issue of unequal allocation of music education resources has largely been overlooked. This study addresses this gap by examining the current status of music resource distribution across colleges and universities in Province C. An objective function was formulated based on four key indicators reflective of music education resources. The Non-dominated Sorting Genetic Algorithm II (NSGA-II), a multi-objective optimization algorithm, was employed to optimize this function and develop a novel resource allocation strategy. Upon implementation of this strategy, the optimized resource quantities at the sampled institutions were observed as follows: 1,131 square meters of space, 809 books, 72 musical instruments, and 7 personnel. Subsequent evaluation of the changes in students’ musical performance, pre-and post-implementation, revealed significant improvements. Specifically, the average music performance score in the experimental group increased by 7.32 points, compared to a 6.63-point increase in the control group, with both enhancements statistically significant as per the t-test results. Further, a survey assessing the perceptions of teachers and students towards the newly implemented program indicated that 66.8% of respondents endorsed the revised allocation. Collectively, these findings suggest that the optimized allocation strategy not only facilitated the music education resource distribution but also garnered a positive reception and improved student outcomes in music, thereby contributing constructively to the overarching goal of strengthening the educational infrastructure.</p>
</abstract>

The equitable distribution of educational resources epitomizes the essence of establishing a robust educational nation. Yet, the issue of unequal allocation of music education resources has largely been overlooked. This study addresses this gap by examining the current status of music resource distribution across colleges and universities in Province C. An objective function was formulated based on four key indicators reflective of music education resources. The Non-dominated Sorting Genetic Algorithm II (NSGA-II), a multi-objective optimization algorithm, was employed to optimize this function and develop a novel resource allocation strategy. Upon implementation of this strategy, the optimized resource quantities at the sampled institutions were observed as follows: 1,131 square meters of space, 809 books, 72 musical instruments, and 7 personnel. Subsequent evaluation of the changes in students’ musical performance, pre-and post-implementation, revealed significant improvements. Specifically, the average music performance score in the experimental group increased by 7.32 points, compared to a 6.63-point increase in the control group, with both enhancements statistically significant as per the t-test results. Further, a survey assessing the perceptions of teachers and students towards the newly implemented program indicated that 66.8% of respondents endorsed the revised allocation. Collectively, these findings suggest that the optimized allocation strategy not only facilitated the music education resource distribution but also garnered a positive reception and improved student outcomes in music, thereby contributing constructively to the overarching goal of strengthening the educational infrastructure.

Research on Optimized Allocation and Use Effect of Music Education Resources Based on MOOC Platform

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The equitable distribution of educational resources epitomizes the essence of establishing a robust educational nation. Yet, the issue of unequal allocation of...

Research on Optimized Allocation and Use Effect of Music Education Resources Based on MOOC Platform

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 09 mar 2024

Accettato: 04 giu 2024

DOI: https://doi.org/10.2478/amns-2024-1733

Parole chiave
NSGA-II, Elite retention strategy, Multi-objective optimization, Music education resource optimization

© 2024 Xiaochun Wu., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on Optimized Allocation and Use Effect of Music Education Resources Based on MOOC Platform

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 09 mar 2024

Accettato: 04 giu 2024

DOI: https://doi.org/10.2478/amns-2024-1733

Parole chiaveNSGA-II, Elite retention strategy, Multi-objective optimization, Music education resource optimization

© 2024 Xiaochun Wu., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
NSGA-II, Elite retention strategy, Multi-objective optimization, Music education resource optimization