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Framelet-based dual hypergraph neural networks for student performance prediction

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Abstract

In the field of educational data mining, accurately predicting student performance is vital for developing effective educational strategies. However, existing methods often fall short in capturing the complex relationships between students, focusing mainly on individual attributes. This paper introduces a pioneering framelet-based dual hypergraph neural network (FD-HGNN) model to advance this task. Our innovative approach decomposes student feature matrices into low-pass and high-pass components using a framelet-based transform. These components form the basis for creating hypergraphs, capturing intricate student relationships. The model integrates a dual hypergraph neural network with distinct channels for low-pass and high-pass components, augmented by a variance interaction layer employing an attention mechanism. This structure ensures a more comprehensive representation of student data, enhancing prediction accuracy. Extensive validation against traditional machine learning methods and graph neural networks across four real-world educational datasets demonstrates the superiority of our approach. The findings highlight the significant potential of our model in revolutionizing student performance prediction in educational settings.

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Data availability

The data used in this study are available upon reasonable request from the authors.

Notes

  1. Without loss of generality, we do not consider the weights of hyperedges in our work. Readers interested in a more detailed study may choose to incorporate hyperedge weights as relevant to their specific tasks.

  2. https://archive.ics.uci.edu/dataset/320/student+performance

  3. https://analyse.kmi.open.ac.uk/open_dataset

  4. http://archive.ics.uci.edu/ml/datasets/turkiye+student+evaluation

  5. http://www.worlduc.com/

  6. https://scikit-learn.org/stable/supervised_learning.html.

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Acknowledgements

Ming Li acknowledges the supports from the Key R &D Program of Zhejiang Province (No. 2024C03262), Zhejiang Provincial Natural Science Foundation (No. LY22F020004), and Jinhua Science and Technology Plan under Grant 2023-3-003a. Jiandong Shi acknowledges a project supported by Scientific Research Fund of Zhejiang Provincial Education Department (No. Y202353674).

Author information

Authors and Affiliations

  1. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, 610041, China

    Yazhi Yang

  2. School of Computer Engineering, Chengdu Technological University, Chengdu, 611730, China

    Yazhi Yang

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yazhi Yang

  4. Department of Computer Science, Zhejiang Normal University, Jinhua, 321004, China

    Jiandong Shi

  5. Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, 321004, China

    Ming Li

  6. Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, 54100, Malaysia

    Hamido Fujita

  7. Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, 18011, Spain

    Hamido Fujita

  8. Regional Research Center, Iwate Prefectural University, Takizawa, 020-0693, Japan

    Hamido Fujita

  9. Zhejiang Institute of Optoelectronics, Jinhua, 321004, China

    Ming Li

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  1. Yazhi Yang
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Correspondence to Ming Li.

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Yang, Y., Shi, J., Li, M. et al. Framelet-based dual hypergraph neural networks for student performance prediction. Int. J. Mach. Learn. & Cyber. (2024). https://doi.org/10.1007/s13042-024-02124-4

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