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IJIET 2020 Vol.10(3): 191-195 ISSN: 2010-3689
doi: 10.18178/ijiet.2020.10.3.1362

Research on the Evaluation Model of Graduate Employment Prospects

Bo Liu, Kelu Yao, Zhengyan Zhao, Shujie Ding, and Hongli Chen

Abstract—The employment situation of graduates can directly reflect the quality of talent training and social recognition of the school. Based on the big data analysis method, learning a graduate employment evaluation model to predict and guide the employment of students in the school. This way not only will improve the employment success rate of graduates but also will improve the talent training ability of universities. This paper collects the employment information of graduates from a university in Beijing in the past three years. We use the Analytic Hierarchy Process to establish an employment evaluation model. more importantly, we learn the student ability distribution in an unsupervised manner, i.e., without a need of annotating records. The experiment shows this manner has some advantage in extreme ratings. In addition, we select fresh graduates who volunteer to participate in the experiment for verification. The college counselor establishes one-on-one employment guidance with students with low employability scores. The final results show that the method has achieved a good performance, and the employment rate of graduates participating in the experiment reaches 100%. Therefore, this method can provide an effective reference for the employment guidance and evaluation of the university.

Index Terms—Employment evaluation, analytic hierarchy process, unsupervised manner, Gaussian mixture model.

The authors are with Beijing University of Technology, China (corresponding author: Zhengyan Zhao; e-mail: boliu@bjut.edu.cn, kelu_yao@163.com, zhaozhengyan@bjut.edu.cn, dingshujie@bjut.edu.cn, chenhongli666@126.com).


Cite: Bo Liu, Kelu Yao, Zhengyan Zhao, Shujie Ding, and Hongli Chen, "Research on the Evaluation Model of Graduate Employment Prospects," International Journal of Information and Education Technology vol. 10, no. 3, pp. 191-195, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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