Abstract
In view of the imperfection of the current music teaching mode system in Colleges and universities, this paper puts forward the design of the multi-cultural music remote auxiliary teaching system based on the mobile Internet of things. Through the combination of emerging mobile Internet of things technology and teaching, optimize the system hardware structure and configuration parameters, further optimize the software function of multi-cultural music remote assistant teaching system based on mobile Internet of things, improve the collection and selection of teaching materials, so as to better improve students’ interest in learning and ensure the quality of teaching.
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References
Huang, C., Yu, K.: Research on the innovation of college music teaching mode based on Artificial Intelligence. J. Phys. Conf. Ser. 1915(2), 022–028 (2021)
Xu, Q., Su, Z., Dai, M., et al.: APIS: privacy-preserving incentive for sensing task allocation in cloud and edge-cooperation mobile internet of things with SDN. IEEE Internet Things J. 7(7), 5892–5905 (2020)
Gren, L.: A flipped classroom approach to teaching empirical software engineering. IEEE Trans. Educ. 63, 155–163 (2020)
Yuan, L., Xiaofei, Z., Yiyu, Q.: Evaluation model of art internal auxiliary teaching quality based on artificial intelligence under the influence of COVID-19. J. Intell. Fuzzy Syst. 39(6), 8713–8721 (2020)
Yang, L.: Comprehensive evaluation of music course teaching level based on improved multi-attribute fuzzy evaluation model. Int. J. Emerg. Technol. Learn. (iJET) 15(19), 107 (2020)
Strong, J.T., Giegold, C.: A multidisciplinary music and visual arts center at Earlham College. J. Acoust. Soc. Am. 145(3), 1739 (2019)
Al-Smadi, M.H.: The effect of using songs on young English learners’ motivation in Jordan. Int. J. Emerg. Technol. Learn. (iJET) 15(24), 52 (2020)
Taheri, A., Meghdari, A., Alemi, M., Pouretemad, H.: Teaching music to children with autism: a social robotics challenge. Sci. Iranica 26(1), 40–58 (2019)
Lin, P.-H., Chen, S.-Y.: Design and evaluation of a deep learning recommendation based augmented reality system for teaching programming and computational thinking. IEEE Access 8, 45689–45699 (2020)
Cano Ortega, A., Sánchez Sutil, F.J., De la Casa Hernández, J.: Power factor compensation using teaching learning based optimization and monitoring system by cloud data logger. Sensors 19(9), 2172 (2019)
Fu, W., Liu, S., Srivastava, G.: Optimization of big data scheduling in social networks. Entropy 21(9), 902 (2019)
Liu, S., Bai, W., Zeng, N., et al.: A fast fractal based compression for MRI images. IEEE Access 7, 62412–62420 (2019)
Liu, S., Li, Z., Zhang, Y., et al.: Introduction of key problems in long-distance learning and training. Mob. Netw. Appl. 24(1), 1–4 (2019)
Martinovi-Bogojevi, J.: Aspekti kolaborativne kreativnosti u nastavi glazbe u osnovnoj koliCollaborative creativity in elementary school music teaching. Metodički Ogledi 27(1), 127–148 (2020)
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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Ren, Yl., Zhou, J., Wu, Yb. (2022). Multi Cultural Music Remote Assistant Teaching System Based on Mobile Internet of Things. In: Liu, S., Ma, X. (eds) Advanced Hybrid Information Processing. ADHIP 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 416. Springer, Cham. https://doi.org/10.1007/978-3-030-94551-0_44
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DOI: https://doi.org/10.1007/978-3-030-94551-0_44
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