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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Aires RFD, Ferreira L, de Araujo AG, Borenstein D (2018) Student selection in a Brazilian university: using a multi-criteria method. J Oper Res Soc 69:528–540. https://doi.org/10.1057/s41274-017-0242-3
Alanzi KA, Alfraih MM (2017) Does accumulated knowledge impact academic performance in cost accounting? J Int Educ Bus 10:2–11. https://doi.org/10.1108/jieb-08-2016-0019
Altunok T, Ozpeynirci O, Kazancoglu Y, Yilmaz R (2010) Comparative analysis of multicriteria decision making methods for postgraduate student selection Egitim Arastirmalari-Eurasian. J Educ Res 10:1–15
Asif R, Merceron A, Ali SA, Haider NG (2017) Analyzing undergraduate students’ performance using educational data mining. Comput Educ 113:177–194. https://doi.org/10.1016/j.compedu.2017.05.007
Bodger O et al (2011) Graduate entry medicine: selection criteria and student performance. Plos One. https://doi.org/10.1371/journal.pone.0027161
Broggini S, Costa F (2017) A survey of English-medium instruction in Italian higher education An updated perspective from 2012 to 2015. J Immer Content Based Lang Educ 5:238–264. https://doi.org/10.1075/jicb.5.2.04bro
Burgos C, Campanario ML, de la Pena D, Lara JA, Lizcano D, Martinez MA (2018) Data mining for modeling students’ performance: a tutoring action plan to prevent academic dropout. Comput Electr Eng 66:541–556. https://doi.org/10.1016/j.compeleceng.2017.03.005
Buyse T, Lievens F (2011) Situational judgment tests as a new tool for dental student selection. J Dent Educ 75:743–749
Chadi A, de Pinto M (2018) Selecting successful students? Undergraduate grades as an admission criterion. Appl Econ 50:3089–3105. https://doi.org/10.1080/00036846.2017.1418072
Chakraborty T, Chattopadhyay S, Chakraborty AK (2018) A novel hybridization of classification trees and artificial neural networks for selection of students in a business school. Opsearch 55:434–446. https://doi.org/10.1007/s12597-017-0329-2
Chan-Ob T, Boonyanaruthee V (1999) Medical student selection: which matriculation scores and personality factors are important? J Med Assoc Thail = Chotmaihet thangphaet 82:604–610
Cheng CH, Liu WX (2017) An appraisal model based on a synthetic feature selection approach for students’ academic achievement. Symm Basel. https://doi.org/10.3390/sym9110282
Christensen DG, Nance WR, White DW (2012) Academic performance in MBA programs: do prerequisites really matter? J Educ Bus 87:42–47. https://doi.org/10.1080/08832323.2011.555790
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https://doi.org/10.1023/a:1022627411411
Costa F, Coleman JA (2013) A survey of English-medium instruction in Italian higher education. Int J Biling Educ Biling 16:3–19. https://doi.org/10.1080/13670050.2012.676621
Deliktas D, Ustun O (2017) Student selection and assignment methodology based on fuzzy MULTIMOORA and multichoice goal programming. Int Trans Oper Res 24:1173–1195. https://doi.org/10.1111/itor.12185
Dobson P, Krapljan-Barr P, Vielba C (1999) An evaluation of the validity and fairness of the graduate management admissions test (GMAT) used for MBA selection in a UK business school. Int J Select Assess 7:196–202. https://doi.org/10.1111/1468-2389.00119
Doh JP (2010) Why aren’t business schools more global and what can management educators do about it? Acad Manag Learn Educ 9:165–168. https://doi.org/10.5465/amle.2010.51428541
Dreher GF, Ryan KC (2002) Evaluating MBA-program admissions criteria: the relationship between pre-MBA work experience and post-MBA career outcomes. Res High Educ 43:727–744
Dreher GF, Ryan KC (2004) A suspect MBA selection model: the case against the standard work experience requirement. Acad Manag Learn Educ 3:87–91. https://doi.org/10.5465/amle.2004.12436822
Duran G, Wolf-Yadlin R (2011) A mathematical programming approach to applicant selection for a degree program based on affirmative action. Interfaces 41:278–288. https://doi.org/10.1287/inte.1100.0542
Edgar F et al (2013) Employing graduates: selection criteria and practice in New Zealand. J Manag Org 19:338–351. https://doi.org/10.1017/jmo.2013.25
Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38:367–378. https://doi.org/10.1016/s0167-9473(01)00065-2
Gupta A, Turek J (2015) Empirical investigation of predictors of success in an MBA programme. Educ Train 57:279–289. https://doi.org/10.1108/et-10-2012-0100
Gupta SK, Gupta S, Vijay R (2013) Prediction of student success that are going to enroll in the higher technical education. Int J Comput Sci Eng Inf Technol Res 3:95–108
Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20:832–844
Hoerl AE, Kennard RW (1970) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 42:55–67. https://doi.org/10.2307/1271436
Jury M, Smeding A, Darnon C (2015) First-generation students’ underperformance at university: the impact of the function of selection. Front Psychol. https://doi.org/10.3389/fpsyg.2015.00710
Kass D, Grandzol C, Bommer W (2012) The GMAT as a predictor of MBA performance: less success than meets the eye. J Educ Bus 87:290–295. https://doi.org/10.1080/08832323.2011.623196
Koch E, Dornbrack J (2008) The use of language criteria for admission to higher education in South Africa: issues of bias and fairness investigated. S Afr Linguist Appl Lang Stud 26:333–350. https://doi.org/10.2989/salals.2008.26.3.3.630
Lechien JR, Kempenaers C, Dramaix M, Linkowski P (2016) Influence of gender and selection procedures on the academic performance of undergraduate medical students. Acta Med Acad 45:145–151. https://doi.org/10.5644/ama2006-124.170
Lu OHT, Huang AYQ, Huang JCH, Lin AJQ, Ogata H, Yang SJH (2018) Applying learning analytics for the early prediction of students’ academic performance in blended learning. Educ Technol Soc 21:220–232
Lueg K, Lueg R (2015) Why do students choose English as a medium of instruction? A Bourdieusian perspective on the study strategies of non-native English speakers. Acad Manag Learn Educ 14:5–30. https://doi.org/10.5465/amle.2013.0009
Marnewick C (2012) The mystery of student selection: are there any selection criteria? Educ Stud 38:123–137. https://doi.org/10.1080/03055698.2011.567041
Miller R, Bradbury J (1999) Academic performance of English first and second language students: selection criteria. S Afr J Sci 95:30–34
Muratov E, Lewis M, Fourches D, Tropsha A, Cox WC (2017) Computer-assisted decision support for student admissions based on their predicted academic performance. Am J Pharm Educ 81:46
Pratt WR (2015) Predicting MBA student success and streamlining the admissions process. J Educ Bus 90:247–254. https://doi.org/10.1080/08832323.2015.1027164
Pretz JE, Kaufman JC (2017) Do traditional admissions criteria reflect applicant creativity? J Creat Behav 51:240–251. https://doi.org/10.1002/jocb.120
Punlumjeak W, Rachburee N, IEEE (2015) A comparative study of feature selection techniques for classify student performance. In: 2015 7th international conference on information technology and electrical engineering
Roberts MJ, Gale TCE, McGrath JS, Wilson MR (2016) Rising to the challenge: acute stress appraisals and selection centre performance in applicants to postgraduate specialty training in anaesthesia. Adv Health Sci Educ 21:323–339. https://doi.org/10.1007/s10459-015-9629-6
Schwager ITL, Hulsheger UR, Bridgeman B, Lang JWB (2015) Graduate Student Selection: graduate record examination, socioeconomic status, and undergraduate grade point average as predictors of study success in a western European University. Int J Select Assess 23:71–79. https://doi.org/10.1111/ijsa.12096
Selber JC, Tong W, Koshy J, Ibrahim A, Liu J, Butler C (2014) Correlation between trainee candidate selection criteria and subsequent performance. J Am Coll Surg 219:951–957. https://doi.org/10.1016/j.jamcollsurg.2014.07.942
Shepherd DA, Douglas EJ, Fitzsimmons JR (2008) MBA admission criteria and an entrepreneurial mind-set: evidence from “Western” style MBAs in India and Thailand. Acad Manag Learn Educ 7:158–172. https://doi.org/10.5465/amle.2008.32712615
Singh V, Chakravarty S (2018) Are quantitative skills critical for business education program or an entry-barrier for diversity? Psychol Stud 63:325–334. https://doi.org/10.1007/s12646-018-0450-1
Rianto, Setyohadi DB, Suyoto, IEEE (2017) AHP-TOPSIS on selection of new university students and the prediction of future employment. In: 2017 1st international conference on informatics and computational sciences
Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30:79–82. https://doi.org/10.3354/cr030079
Wright RE, Palmer JC (1997) Examining performance predictors for differentially successful MBA students. Coll Stud J 31:276
Yustanti W, Anistyasari Y, Imah EM, IEEE (2017) determining student’s single tuition fee category using correlation based feature selection and support vector machine. In: 2017 international conference on advanced computer science and information systems. International conference on advanced computer science and information systems, pp 172–176
Zaffar M, Savita KS, Hashmani MA, Rizvi SSH (2018) A study of feature selection algorithms for predicting students academic performance. Int J Adv Comput Sci Appl 9:541–549
Zamudio-Sanchez FJ, Romo-Lozano JL, Rosa A, Martinez-Gomez G, Avalos-Vargas A (2017) Model of selection and evaluation for graduate applicants in forest sciences. Revista Chapingo Serie Ciencias Forestales Y Del Ambiente 23:353–367. https://doi.org/10.5154/r.rchscfa.2016.12.074
Funding
This work was funded by the Pedagogic Reform Program 2018 in Tongji University (no. 2018GH04003), and the Fundamental Research Funds for the Central Universities of China (no. 22120180306) in Tongji University.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflicts of interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wu, X., Wu, J. Criteria evaluation and selection in non-native language MBA students admission based on machine learning methods. J Ambient Intell Human Comput 11, 3521–3533 (2020). https://doi.org/10.1007/s12652-019-01490-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-019-01490-0