A Q-Learning Approach for Optimizing the Impact of Musical Education Using Virtual Reality and Social Robots

He Fengmei
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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.

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利用虚拟现实和社交机器人优化音乐教育效果的 Q 学习方法
本研究论文探讨了将音乐教育与虚拟现实(VR)、生物反馈和社交机器人等创新技术相结合以增强学生心理健康的潜力。为了优化这些干预措施,并确定它们如何有助于提高音乐教育对心理健康的作用,本文采用了强化学习技术,即 Q-learning 方法。虚拟现实技术用于身临其境的学习,并创造出引人入胜、丰富多彩的练习课程。生物反馈用于实时调整和定义个性化音乐疗法。社交机器人通过促进积极的群体互动来增强群体活力。研究首先从不同教育机构挑选了一批背景各异的学生,并对他们的心理健康基线进行了评估。然后让这些学生参与音乐教育课程,如聆听音乐、学习乐器,并在拟议技术的辅助下开展小组活动。其次,实施监测机制,持续监测学生的心理健康状况并收集反馈数据。第三,利用 Q-learning 技术对收集到的数据进行分析,该技术采用试错法来制定音乐教育的最佳政策。它的工作原理是存储 Q 值,该值代表在给定状态下采取特定行动的预期未来回报。Q 值在干预的每一步都会更新,并以时差误差为基础,将预期奖励与实际奖励进行比较,直到 Q 值收敛。对干预后学生心理健康的结果分析表明,压力水平平均降低了 25%,焦虑水平降低了 20%,抑郁水平降低了 15%。这些指标的降低意味着音乐教育干预产生了积极影响,并凸显了音乐教育在学校课程中的重要性。
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