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Teaching content recommendations in music appreciation courses via graph embedding learning

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International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

The traditional music appreciation course teaching model relies on questionnaires or manual decision-making to determine teaching content, which is time-consuming and easily reduces student satisfaction and teaching quality. How to use artificial intelligence technology to improve the selection process of teaching content is a valuable and important issue. This paper treats the above problem as a teaching content recommendation task and proposes a two-stage graph embedding learning (TSGEL) framework. Specifically, our TSGEL includes three customized modules: (1) a graph convolution module with side information to capture students’ preferences through effective information propagation on student-song graphs; (2) a refined prediction module aims to highlight students’ general preferences, thereby alleviating possible inconsistencies between training and testing distributions; and (3) a teaching content recommendation module selects some songs that can achieve group tradeoffs as teaching content based on the obtained student preferences. The first two modules constitute the individual stage for learning student preferences, and the latter is the group stage for integrating the preferences of all the students to recommend teaching content. Finally, we conduct extensive experiments on a public dataset and a real course dataset to verify the effectiveness and compatibility of our TSGEL.

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Data availability

The real-world dataset used in our paper is confidential, but the other benchmark dataset is publicly available.

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Acknowledgements

This work is supported by Teaching Quality and Teaching Reform Construction Project of Undergraduate Universities in Guangdong Province in 2022: “Research on the Reform of College Music Appreciation Courses Aiming at Improving Students’ Music Aesthetics and Writing Ability”, and The 2022 Shenzhen University Teaching Reform Research Project No. JG2022055.

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Authors and Affiliations

  1. Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Shenzhen, China

    Dugang Liu, Xiaolin Lin & Lingjie Li

  2. Faculty of Education, Shenzhen University, Shenzhen, China

    Zishan Ming

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  1. Dugang Liu
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  2. Xiaolin Lin
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  3. Lingjie Li
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  4. Zishan Ming
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Contributions

Dugang Liu and Xiaolin Lin wrote the main manuscript text and Lingjie Li prepared the experiment. All authors reviewed the manuscript.

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Correspondence to Zishan Ming.

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Liu, D., Lin, X., Li, L. et al. Teaching content recommendations in music appreciation courses via graph embedding learning. Int. J. Mach. Learn. & Cyber. (2024). https://doi.org/10.1007/s13042-024-02123-5

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