Teaching content recommendations in music appreciation courses via graph embedding learning-Reference-Cited by-同舟云学术

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Teaching content recommendations in music appreciation courses via graph embedding learning

Published:2024-05-16 Issue:9 Volume:15 Page:3847-3862
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Liu Dugang,Lin Xiaolin,Li Lingjie,Ming Zishan

Funder

Teaching Quality and Teaching Reform Construction Project of Undergraduate Universities in Guangdong Province in 2022

The 2022 Shenzhen University Teaching Reform Research Project

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s13042-024-02123-5.pdf

Reference44 articles.

1. Zhang Y, An R, Liu S, Cui J, Shang X (2023) Predicting and understanding student learning performance using multi-source sparse attention convolutional neural networks. IEEE Trans Big Data 9(01):118–132

2. Serra R, Martinez C, Vertegaal CJ, Sundaramoorthy P, Bentum MJ (2023) Using student-led tutorials to improve student performance in challenging courses. IEEE Trans Educ 66:339–349

3. Huifang Q, Hao J (2021) The status quo and thinking of classical music education and learning in higher vocational colleges in China. In: Proceedings of the 7th international conference on education and training technologies, pp 90–95

4. Wang F, Wang L, Yu W, Xia F, Zhang E, Su B (2022) Research on the teaching of music education in colleges and universities under the reflective teaching dimension. In: Proceedings of the 7th international conference on information and education innovations, pp 77–83

5. Thompson WF, Bullot NJ, Margulis EH (2023) The psychological basis of music appreciation: structure, self, source. Psychol Rev 130(1):260

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