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).