Criteria evaluation and selection in non-native language MBA students admission based on machine learning methods

Although the research on student selection criteria has been very rich up to now, the role of the level of foreign language played in the admission selection of a non-native spoken program is still receiving little attention. This study intends to explore the issue through three research methods: (1) two-sample test of a hypothesis; (2) multiple linear regression analysis; (3) machine learning algorithms (Ridge regression, SVM, Random forest, GBDT). The case about 549 students enrolled in the Shanghai International MBA Program in China from 2007 to 2014 was used as empirical research samples. Through three methods of analysis and comparison, it was found that Oral English fluency played a key role in the admission selection of the English spoken MBA program in China. It is confirmed that the criteria, such as Rank of the graduated university, Company Nature, Latest Highest Degree, Math Exam, Sponsor (Tuition provider) and Stress management, have very good effect in predicting the final grades of students when graduation. This study also shows that the methods based on machine learning algorithm modeling such as ridge regression and SVM are suitable for student selection decision modeling.