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Mining Campus Big Data: Prediction of Career Choice Using Interpretable Machine Learning Method

Author

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  • Yuan Wang

    (College of Humanities and Law, Beijing University of Chemical Technology, Beijing 100029, China
    School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Liping Yang

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Jun Wu

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Zisheng Song

    (Department of International Exchange and Cooperation, Beijing University of Chemical Technology, Beijing 100029, China)

  • Li Shi

    (China Information Communication Technology Group Corporation, Beijing 100191, China)

Abstract

The issue of students’ career choice is the common concern of students themselves, parents, and educators. However, students’ behavioral data have not been thoroughly studied for understanding their career choice. In this study, we used eXtreme Gradient Boosting (XGBoost), a machine learning (ML) technique, to predict the career choice of college students using a real-world dataset collected in a specific college. Specifically, the data include information on the education and career choice of 18,000 graduates during their college years. In addition, SHAP (Shapley Additive exPlanation) was employed to interpret the results and analyze the importance of individual features. The results show that XGBoost can predict students’ career choice robustly with a precision, recall rate, and an F 1 value of 89.1%, 85.4%, and 0.872, respectively. Furthermore, the interaction of features among four different choices of students (i.e., choose to study in China, choose to work, difficulty in finding a job, and choose to study aboard) were also explored. Several educational features, especially differences in grade point average (GPA) during their college studying, are found to have relatively larger impact on the final choice of career. These results can be of help in the planning, design, and implementation of higher educational institutions’ (HEIs) events.

Suggested Citation

  • Yuan Wang & Liping Yang & Jun Wu & Zisheng Song & Li Shi, 2022. "Mining Campus Big Data: Prediction of Career Choice Using Interpretable Machine Learning Method," Mathematics, MDPI, vol. 10(8), pages 1-18, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1289-:d:792690
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    References listed on IDEAS

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    Cited by:

    1. Gaeithry Manoharam & Mohd Shareduwan Mohd Kasihmuddin & Siti Noor Farwina Mohamad Anwar Antony & Nurul Atiqah Romli & Nur ‘Afifah Rusdi & Suad Abdeen & Mohd. Asyraf Mansor, 2023. "Log-Linear-Based Logic Mining with Multi-Discrete Hopfield Neural Network," Mathematics, MDPI, vol. 11(9), pages 1-30, April.
    2. Wen Zhang & Xiaofeng Xu & Jun Wu & Kaijian He, 2023. "Preface to the Special Issue on “Computational and Mathematical Methods in Information Science and Engineering”," Mathematics, MDPI, vol. 11(14), pages 1-4, July.

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