Exploring influential factors in Chinese engineering professors' industrial experience: A big data and CV analysis approach.

ABSTRACT

There is a long-standing criticism in academic circles about the inadequacy of engineering industrial experience for professors in Chinese research universities, but there is a lack of analysis of the manifestations and root causes of the problem. The research aims to contribute meaningful insights into the factors influencing the industrial experience of engineering professors in Chinese research universities, facilitating informed policymaking and fostering collaborative efforts between academia and industry. This paper examines Chinese engineering professors' industrial experience using big data mining, computing, modeling, and image rendering. The data was collected from the publicly available CVs from the websites of various Chinese universities which contains data on colleges and universities across China and analyzed engineering professors' professional trajectories and industrial experience using the method of resume analysis. A nonlinear logistic regression model is fitted to determine the impact of multiple independent variables on the probability of engineering industrial experience among professors, with logit transformation and maximum likelihood estimation. The results show that a low percentage of engineering university professors in Chinese research universities have had industry employment after obtaining their Ph.D. degrees. The regression model indicates that gender, level of inbreeding, overseas study experience, university level, and birth age significantly affect the engineering experience of professors. This study proposes several policy recommendations based on comparative analysis and its research. The study's findings are critical for policymakers to create policies that promote industrial partnerships as a fundamental aspect of the professional development of engineering faculty and for further research in the field.