Music education is crucial to cognitive and emotional well-being. Nevertheless, the traditional approaches are not personalized and are unable to dynamically respond to the affective states of the learners, restricting their influence on the emotional interest. As a solution to this void, we will design an Adaptive Emotion-Aware Music Education System (AEMES) which can use the techniques of machine learning to monitor, model and respond to the real-time emotions of learners. The originality of this framework is that it is based on an integrated combination of emotion modeling, adaptive strategy selection, and personalized intervention mechanisms that allow the system to achieve continuous engagement of learners and promote emotional wellbeing. The suggested approach is strictly tested on a variety of datasets that include multimodal responses of learners, which include physiological data, facial expressions and behavioral interactions. In comparison with state-of-the-art methods that include CNN-LSTM, Audio-Transformer, and Visual-Transformer models, it is shown that AEMES performs much better than the existing methods in standard performance measures. The proposed framework has a quantitative performance of an accuracy of 87.2%, F1-score of 85.7% and AUROC of 0.91 compared to baseline models, which have a margin of over 4-8% across measures. The role of emotion modeling and adaptive strategies to the overall performance of the system is also confirmed by ablation studies. Emotional pathways (both in terms of time trends and multi-emotional interactions) visualization illustrates the possibility of the framework to dynamically control emotion of learners and ensure that it does not go negative during learning sessions. The overall results support the claim that AEMES is effective in providing personalized, emotion-sensitive music education, which would prove to be a powerful method of improving the learning process and emotional wellbeing.
Deng et al. (Sat,) studied this question.