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

Yuan Wang, Liping Yang, Jun Wu, Zisheng Song and Li Shi
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Yuan Wang: College of Humanities and Law, 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

Mathematics, 2022, vol. 10, issue 8, 1-18

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.

Keywords: career choice; prediction; machine learning; college students (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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