Exploration Of The Correlation Between Students' Musical Interest And Academic Performance Based On Deep Learning

Abstract

This research delves into the intricate relationship between students' musical interests and their academic performance, leveraging deep learning methodologies for a nuanced analysis. By employing advanced computational techniques, the study aims to uncover patterns, correlations, and potential influences that exist between musical engagement and academic achievement. Key objectives include utilizing deep learning algorithms to analyze large datasets of students' musical interests and academic outcomes, identifying patterns that indicate a correlation, and exploring potential causal factors. The research aims to provide valuable insights into how fostering musical interests may positively impact academic performance, contributing to the broader discourse on the intersections between arts education and scholastic achievement. The findings of this exploration are anticipated to offer practical applications for educators, policymakers, and researchers seeking to understand and optimize the relationship between students' extracurricular interests, such as music, and their overall academic success.