Ping HuThis email address is being protected from spambots. You need JavaScript enabled to view it.
College of Music, Jianghan University, Wuhan430056, China
Received: September 13, 2025 Accepted: November 2, 2025 Publication Date: January 29, 2026
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Music education, a must-have for a complete curriculum, still holds the ground despite the technical innovations of the digital age. The Internet of Things (IoT) has the potential to make a remarkable transformation in music education due to its capability of improving accessibility, enhancing learning experiences and providing real time interaction among students, teachers, and musical instruments. The study intends to create a music education platform based on IoT and evaluate the teaching of music in terms of its effectiveness. In the past years, music education was faced with technical difficulties that were due to IoT like data privacy and security issues. To tackle this problem, the research proposes an Enhanced Coyote Optimizer with Resilient Decision Tree (ECO-RDT) model which aims to get a better learning experience and teaching evaluation. The learning environment that the platform offers through the integration of sensors and IoT-enabled musical instruments is one that is interactive and adaptable. The music education program was strategically placed, skill development, engagement, and performance outcomes being the main foci and instrument data collection was made in terms of Pitch, rhythm, and dynamics. The signals in the data were pre-processed first by band-pass filtering to get rid of the noise. Then, the gamma tone frequency cepstral coefficient (GFCC) is applied to obtain sound features from the preprocessed data. ECO is used to fine-tune the parameters which not only speeds up its convergence but also increases its robustness, while RDT is employed for the assessment of teaching effectiveness. To assess teaching efficacy, the proposed system compares real-time performance data with current teaching algorithms, achieving very precise results in differentiating student performance as the 10-fold cross-validation average of the proposed method (0.965604). This innovative approach drastically boosts teaching effectiveness and student engagement and provides new insights into the future of intelligent music education.
Keywords: IoT-based music education platform, Internet of Things (IoT), Teaching Effectiveness, Enhanced Coyote Optimizer with Resilient Decision Tree (ECO-RDT).
[1] W.Liu,(2020)“Integrating data mining with the Internet of Things to build the music project management system" Journal of Ambient Intelligence and Humanized Computing: 1–13. DOI: https: //doi.org/10.1007/s12652-020-02327-x.
[2] L. Li and Z. Han, (2023) “Design and innovation of audio IoT technology using music teaching intelligent mode" Neural Computing and Applications 35(6): 4383–4396. DOI: https: //doi.org/10.1007/s00521-022-07025-7.
[3] Q. Zhu, (2023) “Research on an online teaching platform for college music courses based on Internet of Things technology" International Journal of Information and Communication Technology 23(1): 1–14. DOI: https: //doi.org/10.1504/IJICT.2023.132158.
[4] J. Wei, M. Karuppiah, and A. Prathik, (2022) “College music education and teaching based on AI techniques" Computers and Electrical Engineering 100: 107851. DOI: https: //doi.org/10.1016/j.compeleceng.2022.107851.
[5] X.Yu,N.Ma,L.Zheng,L.Wang,andK.Wang,(2023) “Developments and applications of artificial intelligence in music education" Technologies 11(2): 42. DOI: https: //doi.org/10.3390/technologies11020042.
[6] J. Sun, (2020) “Research on resource allocation of vocal music teaching systems based on mobile edge computing" ComputerCommunications160:342–350. DOI: https://doi.org/10.1016/j.comcom.2020.05.016.
[7] L. Liu,(2024) “Current situation and innovative methods of brass music teaching based on network information technology" Journal of Electrical Systems 20(1): DOI: https: //doi.org/10.52783/jes.683.
[8] H. Zhao, L. Narikbayeva, and Y. Wu, (2024) “Interactive systems for music composition: A new paradigm in music education" Interactive Learning Environments 32(4): 1194–1203. DOI: https: //doi.org/10.1080/10494820.2022.2115081.
[9] J. Pitt, (2024) “Introduction to the special issue: Posthuman perspectives for music education research" Music Education Research: 1–4. DOI: https://doi.org/10.1080/14613808.2024.2354997.
[10] H. Yan, (2022) “Design of online IoT-based music education platform based on artificial intelligence and multiuser detection algorithm" Computational Intelligence and Neuroscience 2022(1): 9083436. DOI: https: //doi.org/10.1155/2022/9083436.
[11] X. Cui and M. Chen, (2024) “A novel learning frame work for vocal music education: An exploration of convolutional neural networks and pluralistic learning approaches" Soft Computing 28(4): 3533–3553. DOI: https: //doi.org/10.1007/s00500-023-09618-3.
[13] J. Liu and F. Fu, (2023) “Convolutional neural network model by deep learning and teaching robot in keyboard musical instrument teaching" PLoS One 18(10): e0293411. DOI: https: //doi.org/10.1371/journal.pone.0293411.
[14] P. P. Li and B. Wang, (2024) “Artificial intelligence in music education" International Journal of Human Computer Interaction 40(16): 4183–4192. DOI: https://doi.org/10.1080/10447318.2023.2209984.
[15] K. Liu, (2023) “Exploring the blended learning model of music teaching skills for teacher trainees based on teacher training professional certification in the context of big data" Applied Mathematics and Nonlinear Sciences: DOI: https: //doi.org/10.2478/amns.2023.2.00574.
[16] T. Zhang, (2023) “The establishment and practice of a blended teaching model for piano music education" Ap plied Mathematics and Nonlinear Sciences 9(1): DOI: https: //doi.org/10.2478/amns-2024-0819.
[17] Y. Quan, (2020) “Development of computer-aided class room teaching system based on machine learning prediction and artificial intelligence KNN algorithm" Journal of Intelligent and Fuzzy Systems 39(2): 1879–1890. DOI: https: //doi.org/10.3233/JIFS-179959.
[18] S. Sun, (2021) “Evaluation of potential correlation of piano teaching using edge-enabled data and machine learning" Mobile Information Systems 2021(1): 6616284. DOI: https: //doi.org/10.1155/2021/6616284.
[19] W.ShuoandM.Ming, (2022) “Exploring online intelligent teaching methods with machine learning and SVM algorithm" Neural Computing and Applications 34(4): 2583–2596. DOI: https: //doi.org/10.1007/s00521-021-05846-6.
[20] B. Wenjie, (2024) “Simulation of vocal teaching platform based on target speech extraction algorithm and cloud computing e-learning" Entertainment Computing 50: 100700. DOI: https: //doi.org/10.1016/j.entcom.2024.100700.
[21] P. Li, T. M. Liang, Y. M. Cao, X. M. Wang, X. J. Wu, and L. Y. Lei, (2024) “A novel Xi’an drum music generation method based on Bi-LSTM deep reinforcement learning" Applied Intelligence 54(1): 80–94. DOI: https: //doi.org/10.1007/s10489-023-05195-y.
[22] Z. Yiting and Y. Sonquan, (2024) “Modern technology enabled approaches in preschool music education" Inter active Learning Environments 32(1): 156–167. DOI: https://doi.org/10.1080/10494820.2022.2081211.
[23] Y. Hou, (2022) “Research on piano informatization teaching strategy based on deep learning" Mathematical Problems in Engineering 2022(1): 5817752. DOI: https: //doi.org/10.1155/2022/5817752.
[24] S. R. K. S. and G. S. C. S., (2022) “A Framework for Smart Classroom System Based on IoT" Journal of Ap plied Science and Engineering 25(5): 1091–1100.
[25] S. K. S. and S. P. K. J., (2023) “Artificial Intelligence Based Personalized Learning System for Improving Stu dent Engagement and Learning Outcomes" Journal of Applied Science and Engineering 26(4): 543–552.
[26] A. K. M.andR.S., (2023) “An Intelligent Assessment System for Student Performance Using Machine Learning Techniques" Journal of Applied Science and Engineering 26(3): 415–424.
[27] M. T. and D. S., (2023) “Design and Implementation of an Intelligent System for Resource Optimization in Educational Institutes" Journal of Applied Science and Engineering 26(2): 287–296.
[28] B. R. Gudivaka, (2021) “Designing AI-assisted music teaching with big data analysis" Current Science and Humanities 9(4): 1–14.
[29] S.H.Grandhi, (2022) “Enhancing children’s health monitoring: Adaptive wavelet transform in wearable sensor IoT integration" Current Science and Humanities 10(4): 15–27.
[30] M. Ananthi, R. Lakshmana Kumar, B. Muthu, and P. Punitha, (2025) “Adaptive marine intelligence and sensing architecture for autonomous underwater ecosystem monitoring using AI and IoT integration" Intelli gent Data Analysis: 1088467X251339271. DOI: https: //doi.org/10.1177/1088467X251339271.
[31] Kaggle. Music education performance data. https://www.kaggle.com/datasets/ziya07/music education-performance-data/data.[Accessed: September 2, 2025].
[32] X. Xia and J. Yan, (2021) “Construction of music teaching evaluation model based on weighted naïve Bayes" Scientific Programming 2021(1): 7196197. DOI: https: //doi.org/10.1155/2021/7196197.
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