Data Mining-driven Multiple Regression Analysis of Cultural Identity in International Students’ Perceptions of Chinese Culture

Abstract

A multiple regression fitting analysis was conducted to investigate the level of cultural identification of 222 international students studying in China with respect to Chinese culture. The dependent variables are the Chinese Culture Integration Will (CCIW) and the Chinese Culture Integration Degree (CCID). A number of independent variables are taken into account, including gender, age, and religion. A cost-benefit approach is employed, with management convergence (MC) on the cost side, and the intention to work in China (IWC) on the benefit side. Based on data mining and regression model fitting, we use the software program of SPSS to compute the KMO of the questionnaire data, make factor analysis, and then utilize the econometrics software package to make stepwise ordinary least squares estimation. The results indicate that active involvement in school cultural clubs, as well as aspirations for employment within China, significantly influence students’ willingness to comprehend Chinese culture. In contrast, personal characteristics such as gender, age, and nationality appear to have a negligible impact. In order to significantly strengthen students’ cultural identities, they need to become proficient in Chinese language, interact more frequently with Chinese peers, participate in cultural activities at school, and develop effective assimilation strategies. The desire to work in China does not significantly contribute to the integration of cultures. It should be noted that the quality of instruction in courses related to Chinese culture as well as participation in extracurricular activities off-campus do not significantly enhance the level of cultural identification among students. By analyzing big data on international students in China, it is possible to gain insight into international student education and Sino-foreign exchanges.