{"title":"A Q-Learning Approach for Optimizing the Impact of Musical Education Using Virtual Reality and Social Robots","authors":"He Fengmei","doi":"10.1007/s11036-024-02375-z","DOIUrl":null,"url":null,"abstract":"<p>This research paper investigates the potential of combining musical education with innovative technologies like Virtual Reality (VR), Biofeedback, and social robots to enhance student mental health. To optimize these interventions and ascertain how are they helpful in improving the role of musical education on mental health a reinforcement learning technique namely the Q-learning approach is used. VR is used for immersive learning and creates engaging and varied practice sessions. Biofeedback for real-time adjustment and defining personalized music therapy. Social robots are used to enhance group dynamics by facilitating positive group interactions. The study begins by selecting a group of students of diverse backgrounds from different educational institutions and evaluating their baseline mental health. These students were then engaged in musical education sessions like listening to music, learning musical instruments, and group activities assisted by the proposed technologies. Secondly, a monitoring mechanism is implemented that continuously monitors student’s mental health and collects feedback data. Thirdly, the collected data is analyzed using the Q-learning technique, which uses a trial-and-error approach to formulate optimal policy for musical education. It works by storing Q-value, a value that represents the expected future rewards for taking specific actions in a given state. The Q-values are updated at each step of the intervention and are based on the temporal difference error, which compares the expected reward with the actual reward obtained until the Q-value converges. 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 in school curricula.</p>","PeriodicalId":501103,"journal":{"name":"Mobile Networks and Applications","volume":"7 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mobile Networks and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s11036-024-02375-z","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This research paper investigates the potential of combining musical education with innovative technologies like Virtual Reality (VR), Biofeedback, and social robots to enhance student mental health. To optimize these interventions and ascertain how are they helpful in improving the role of musical education on mental health a reinforcement learning technique namely the Q-learning approach is used. VR is used for immersive learning and creates engaging and varied practice sessions. Biofeedback for real-time adjustment and defining personalized music therapy. Social robots are used to enhance group dynamics by facilitating positive group interactions. The study begins by selecting a group of students of diverse backgrounds from different educational institutions and evaluating their baseline mental health. These students were then engaged in musical education sessions like listening to music, learning musical instruments, and group activities assisted by the proposed technologies. Secondly, a monitoring mechanism is implemented that continuously monitors student’s mental health and collects feedback data. Thirdly, the collected data is analyzed using the Q-learning technique, which uses a trial-and-error approach to formulate optimal policy for musical education. It works by storing Q-value, a value that represents the expected future rewards for taking specific actions in a given state. The Q-values are updated at each step of the intervention and are based on the temporal difference error, which compares the expected reward with the actual reward obtained until the Q-value converges. 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 in school curricula.