Criteria evaluation and selection in non-native language MBA students admission based on machine learning methods
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ORIGINAL RESEARCH
Criteria evaluation and selection in non‑native language MBA students admission based on machine learning methods Xiaojun Wu1 · Jing Wu1 Received: 10 May 2019 / Accepted: 9 September 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract 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. Keywords Machine learning · MBA admission selection · Non-native language · Performance prediction
1 Introduction The main aim of all educational organizations is to improve the quality of education and elevate the academic performance of students (Zaffar et al. 2018). In particular, the admission process of students to a postgraduate program is a very important for the quality improvement of a program (Zamudio-Sanchez et al. 2017). Specifically, the selection of quality students for Master of Business Administration (MBA) programs to ensure that the target placement percentage is achieved and the excellent reputation of an institute is maintained is a common problem (Chakraborty et al. 2018). Entry into graduate programs is highly competitive (Edgar et al. 2013). A systematic evaluation of current MBA admissions is the first step in ensuring that program graduates are prepared to meet the needs of employing organizations (Dreher and Ryan 2004). Although there are many studies on the selection of MBA students, the literature * Jing Wu [email protected] 1
School of Economics and Management, Tongji University, Shanghai, People’s Republic of China
in this field ignores the students of MBA programs that teach in a non-native language. Take the Chinese education market as an example. On the one hand, there are a large number of international students who travel abroad for higher education every year. On the other hand, many Chinese higher education institutions cooperate with famous foreign universities to introduce their educational resources in China
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