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Multi-level contrastive graph learning for academic abnormality prediction

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Abstract

Academic Abnormality Prediction aims to predict whether students have academic abnormalities through their historical academic scores. However, existing research methods still have the following challenges: (1) Student behavior. Only the students’ historical academic performance is considered, ignoring the impact of student behavior in student status. (2) Data imbalance. The number of academically abnormal students is much less than that of ordinary students, resulting in a data imbalance problem. Therefore, in response to the above challenges, this paper proposes a Multi-level Contrastive Graph learning for academic abnormality prediction (MCG). Specifically, firstly, we capture student behavior and fuse it with student historical achievement data based on a Graph Neural Network (GNN), Thereafter, we construct an embedding space for sample interpolation, which generates virtual nodes of abnormal students, thereby alleviating the data imbalance problem. Moreover, we introduce a multi-level contrastive learning module to precisely learn node representations and maximize the consistency between different views of the same node in the target and online networks for data augmentation. Experiments on real datasets show that the abnormality prediction performance of MCG outperforms the existing state-of-the-art methods.

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Data availability

The datasets generated during and analyzed during the current study are available in the repository, [https://github.com/wyl688/MCG]. We obtained the consent of the dataset authors.

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Acknowledgements

This work described in this paper was supported by the National Natural Science Foundation of China (Grant No. 62106070). This work is also support by the Open Foundation of State Key Laboratory for Novel Software Technology at Nanjing University of P.R.China (No.KFKT2021B12) and the Key Research and Development plan of Hubei Province (2020BAB012).

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Authors and Affiliations

  1. School of Computer Science, Hubei University of Technology, Wuhan, 430068, China

    Yong Ouyang, Yuanlin Wang, Rong Gao, Jinhang Liu & Zhiwei Ye

  2. State Key Laboratory of Novel Software Technology at Nanjing University, Nanjing University, Nanjing, 210023, China

    Rong Gao

  3. Tencent Inc, Shenzhen, 518000, China

    Yawen Zeng

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  1. Yong Ouyang
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  2. Yuanlin Wang
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Correspondence to Rong Gao.

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Ouyang, Y., Wang, Y., Gao, R. et al. Multi-level contrastive graph learning for academic abnormality prediction. Neural Comput & Applic 36, 3681–3698 (2024). https://doi.org/10.1007/s00521-023-09268-4

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