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Elective future: The influence factor mining of students’ graduation development based on hierarchical attention neural network model with graph

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Abstract

The graduation development such as employment and graduate school admission of college students are important tasks. However, mining the factors that can affect the development of graduation remains challenging, because the most important factor “course” is not independent and inequality, which are always ignored by previous researchers. Furthermore, traditional structured methods cannot handle the complex relationships between courses, and attention networks cannot distinguish the weights of compulsory and elective courses with different distributions. Therefore, we present a Graph-based Hierarchical Attention Neural Network Model with Elective Course (GHANN-EC) for the prediction of graduation development in this study. Specifically, we use graph embedding that captures the unstructured relationships between courses and hierarchical attention that assigns the importance of the courses to excavating course information that represent students’ independent interests, and can more accurately understand the relationship between graduation development and academic performance. Experimental results on the real-world datasets show that GHANN-EC outperforms the existing popular approach.

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Acknowledgements

We gratefully acknowledge the support of National Natural Science Foundation of China (Grant No.61772180). This work is also supported by the funds for Science and Technology Department of Hubei Province (Grant No. 2013CFB021), the teaching research project of Hubei Province (Grant No.2018316), the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (17KJB520028), NUPTSF (NY217114), Tongda College of Nanjing University of Posts and Telecommunications (XK203XZ18002), Qing Lan Project of Jiangsu Province, and Doctoral Scientific Research Foundation of Hubei University of Technology (BSQD2019026).

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Authors and Affiliations

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

    Yong Ouyang, Yawen Zeng, Rong Gao & Chunzhi Wang

  2. College of Tongda, Nanjing University of Posts and Telecommunications, Nanjing, China

    Yonghong Yu

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  1. Yong Ouyang
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  2. Yawen Zeng
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Correspondence to Yong Ouyang.

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Ouyang, Y., Zeng, Y., Gao, R. et al. Elective future: The influence factor mining of students’ graduation development based on hierarchical attention neural network model with graph. Appl Intell 50, 3023–3039 (2020). https://doi.org/10.1007/s10489-020-01692-6

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