Abstract
This paper demonstrates the application of educational data mining in predicting applicant’s enrollment decision for academic programme in higher learning institution. This research specifically aims to address the application of data mining on higher education institution database to understand student enrolment data and gaining insights into the important factors in making enrollment decision. By adapting the five phases of the Cross Industry Standard Process for Data Mining (CRISP-DM) process model, detail explanations of the activities conducted to execute the data analytics project are discussed. Predictive models such as logistic regression, decision tree and naïve bayes were built and applied to process the data set. Subsequently, these models were tested for accuracy using 10-fold cross validation. Results show that, given adequate data and appropriate variables, these models are capable of predicting applicant’s enrollment decision with roughly 70% accuracy. It is noted that decision tree model yields the highest accuracy among the three prediction models. In addition, different significant factors are identified for different type of academic programmes applied as suggested by the findings.
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Lotfi, V., Maki, B.: A predictive model for graduate application to enrollment. Open Access Libr. J. 5(04), 1 (2018)
Hoyt, J.E., Brown, A.B.: Identifying college choice factors to successfully market your institution. Coll. Univ. 78(4), 3 (2003)
Schoenherr, H.J.: Beyond academic reputation: factors that influence the college of first choice for high achieving students. University of South Florida (2009)
Abou-Nassif, S.M.: Self-reported factors that influence choice of college-bound students in Lebanon. Capella University (2011)
Khan, M.M., Zia-ur-Rehman, C., Khan, S.S.: Factors influencing students’ choice of private college/university in Pakistan. New Horiz. 10(2), 69 (2016)
Joseph, M., Spake, D.F., Albrecht, C.M.: Branding universities: an updated view of factors underlying college choice. In: The Customer is NOT Always Right? Marketing Orientations in a Dynamic Business World, pp. 370–372. Springer, Cham (2017)
Ryan, Y., Koech, J., Koech, M.J.: Factors influencing the choice of college among undergraduate students in public universities in Kenya. A case study of the university of Eldoret. Dalam Aust. J. Commer. Study. University of Eldoret, Kenya (2010)
Hayes, J.J.: Increasing Enrollment: Evaluating College-Choice Factors at a Midwest Christian University (2014)
Sidin, S.M., Hussin, S.R., Soon, T.H.: An exploratory study of factors influencing the college choice decision of undergraduate students in Malaysia. Asia Pac. Manag. Rev. 8(3), 259–280 (2003)
O’neil, B.: College choice: Factors influencing high school seniors’ decision to enroll at private colleges in South Carolina (Doctoral dissertation, Clemson University) (2013)
Wanjau, S.K.: Data mining model for predicting student enrolment in STEM courses in higher education institutions (2016)
Siraj, F., Abdoulha, M.A.: Mining enrolment data using predictive and descriptive approaches. Knowl.-Oriented Appl. Data Min. 53–72 (2007)
Abelt, J., Browning, D., Dyer, C., Haines, M., Ross, J., Still, P., Gerber, M.: Predicting likelihood of enrollment among applicants to the UVa undergraduate program. In: Systems and Information Engineering Design Symposium (SIEDS), pp. 194–199. IEEE (2015)
Djunaidi, H.: Predicting student enrollments using SAS. In: Proceedings of South Central SAS Users Group Annual Conference (2012)
Zaytsev, M.: Predicting enrollment decisions of students admitted to claremont McKenna college (2011)
Sampath, V., Flagel, A., Figueroa, C.: A logistic regression model to predict freshmen enrollments. In: annual meeting of the SouthEast SAS Users Group (2009). http://analyticsncsu.edu/sesug/2009/SD016.Sampath.pdf
Mulugeta, M., Borena, B.: Higher education students’ enrolment forecasting system using data mining application in Ethiopia. HiLCoE J. Comput. Sci. Technol. 2(2), 37 (2013)
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: CRISP-DM 1.0 Step-by-step data mining guide (2000)
Burkhardt, J.C., DesJardins, S.L., Teener, C.A., Gay, S.E., Santen, S.A.: Predicting medical school enrollment behavior: comparing an enrollment management model to expert human judgment. Acad. Med. 93(11S), S68–S73 (2018)
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Ab Ghani, N.L., Che Cob, Z., Mohd Drus, S., Sulaiman, H. (2019). Student Enrolment Prediction Model in Higher Education Institution: A Data Mining Approach. In: Othman, M., Abd Aziz, M., Md Saat, M., Misran, M. (eds) Proceedings of the 3rd International Symposium of Information and Internet Technology (SYMINTECH 2018). SYMINTECH 2018. Lecture Notes in Electrical Engineering, vol 565. Springer, Cham. https://doi.org/10.1007/978-3-030-20717-5_6
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DOI: https://doi.org/10.1007/978-3-030-20717-5_6
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