Assessing Psychological Health and Emotional Expression of Musical Education Using Q-Learning

Musical education has a positive impact on psychological health. It enhances emotional expression and contributes to constructive transformation of mental health. This study explores the use of a machine learning technique known as Q-learning to assess these effects. The research process commences by collecting data from music students. This data includes psychological health status, emotional expression levels and progress in musical education. Surveys and regular assessments are used for this purpose in which Students report their psychological health and emotional experiences. It also tracks and record their progress in musical education. Secondly, a Q-learning algorithm is implemented to analyze the collected data. It demonstrates how different musical education activities influence psychological health and emotional expression. The algorithm works in the form of iterations and can learn from interactions and make decisions based on rewards. Thirdly, the algorithm processes the information and identifies which activities have the most positive impact on musical education by identifying patterns. It also assists in suggesting different types of improvements and methods in teaching methods. To evaluate the performance of the study different performance metrics are used. These indicators include psychological health scores, levels of emotional expression, progress in music skills, attendance rates, participation in class activities and student engagement levels. It also depicts what kinds of activities are particularly beneficial in increasing impact of the musical education. The study shows that students deeply engaged in music have better psychological health and exhibit higher levels of emotional expression.