Optimizing the Impact of Musical Education on Mental Health of Students using Q-Learning

Mental health of students is growing concern in today's world and require immediate attention and effective interventions in form of innovative techniques and models. Music education can be used as a promising solution to mental health problems because of its therapeutic effects. This research paper explore the effects of musical education on mental health using a reinforcement learning technique namely Q-learning approach. The study begins by selecting a group of students of diverse background from different educational institutions and evaluate their baseline mental health. These students were then engaged in musical education sessions like listening to music, learning musical instruments, and group activities. Secondly, a monitoring mechanism is emplaced that continuously monitor student's mental health and collect feedback data. Thirdly, the collected data is analyzed using Q-learning technique which uses trial and error approach to formulate optimal policy for music education. It work by storing Q-value, a value which represent the expected future rewards for taking specific actions in a given state. The Q-values is updated at each step of the intervention and is based on the temporal difference error which compares the expected reward with the actual reward obtained. The Q-values converge to the optimal values after a number of iterations which indicate the best actions to take in each state to maximize the cumulative reward. The results analysis of student's mental health following the intervention showed that stress levels decreased by an average of 25%, anxiety levels decreased by 20%, and depression levels decreased by 15%. Reductions in these metrics imply the positive impact of musical education intervention and highlight the importance of musical education into school curricula.