A Machine Learning Based DSS in Predicting Undergraduate Freshmen Enrolment in a Philippine University

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2021 by IJCTT Journal
Volume-69 Issue-5
Year of Publication : 2021
Authors : Dr. Joseph A. Esquivel, Dr. James A. Esquivel
  10.14445/22312803/IJCTT-V69I5P107

MLA Style: 
Dr. Joseph A. Esquivel and Dr. James A. Esquivel. "A Machine Learning Based DSS in Predicting Undergraduate Freshmen Enrolment in a Philippine University."  International Journal of Computer Trends and Technology,  vol. 69, no. 5, May. 2021, pp.50-54.  Crossref https://doi.org/ 10.14445/22312803/IJCTT-V69I5P107

APA Style:   
Dr. Joseph A. Esquivel & Dr. James A. Esquivel 
(2021) . A Machine Learning Based DSS in Predicting Undergraduate Freshmen Enrolment in a Philippine University.  International Journal of Computer Trends and Technology , 69(5), 50-54. https://doi.org/ 10.14445/22312803/IJCTT-V69I5P107

Abstract
The sudden change in the landscape of Philippine education, including the implementation of K to 12 program, Higher Education institutions, have been struggling in attracting freshmen applicants coupled with difficulties in projecting incoming enrollees. Private HEIs Enrolment target directly impacts success factors of Higher Education Institutions. A review of the various characteristics of freshman applicants influencing their admission status at a Philippine university were included in this study. The dataset used was obtained from the Admissions Office of the University via an online form which was circulated to all prospective applicants. Using Logistic Regression, a predictive model was developed to determine the likelihood that an enrolled student would seek enrolment in the institution or not based on both students and institution’s characteristics. The LR Model was used as the algorithm in the development of the Decision Support System. Weka was utilized on selection of features and building the LR model. The DSS was coded and designed using R Studio and R Shiny which includes data visualization and individual prediction.

Keywords
Data Mining, Education Data Mining, Machine Learning, Predictive Modeling, Binary Classification, Logistic Regression.

Reference

[1] J. Abelt, D. Browning, C. Dyer, M. Haines, J. Ross, P. Still, and M. Gerber, Predicting likelihood of enrollment among applicants to the UVa undergraduate program (2015). 194–199. [Online]. Available: https://doi.org/10.1109/sieds.2015.7116973.
[2] M. Barthelson, I.Boumlic, &U. Shamma. Design to improve the freshman admissions process. 2014 IEEE Systems and Information Engineering Design Symposium, SIEDS 2014, (2014) 124–128. https://doi.org/10.1109/SIEDS.2014.6829878
[3] K. Basu, T. Basu, R. Buckmire, and N. Lal Predictive Models of Student College Commitment Decisions Using Machine Learning. Data,4(2) (65) (2019). [Online].Available: https://doi.org/10.3390/data4020065. 2019
[4] L. Lapovsky. The Changing Business Model For Colleges And Universities. Forbes 2018. Available online: https://www.forbes.com/sites/lucielapovsky/2018/02/06/thechanging-business-model-for-colleges-and-universities #bbc03d45ed59 (accessed on 1 October 2020).
[5] C. JalotaandR. Agrawal.. Analysis of Educational Data Mining using Classification. (2019)243-247. 10.1109/COMITCon.2019.8862214.
[6] S. Hussain andD. Abdulaziz, Neamaand Ba-Alwib, FadlandNajoua, Ribata.. Educational Data Mining and Analysis of Students' Academic Performance Using WEKA. Indonesian Journal of Electrical Engineering and Computer Science. 9. 447-459. 10.11591/ijeecs.v9.i2.(2018)447-459.
[7] S. B. Kotsiantis, I. D. ZaharakisandP. E. Pinelas. Machine learning: A review of classification and combiningtechniques. Artificial Intelligence Review, 26 (2006) 159–190.
[8] N. Undaiva, P. Doliaand N. P. Shah. Education Data Mining in Higher Education - A Primary Prediction Model and Its Affecting Parameters International Journal of Current Research, 5(5)(2013)1209–1213. https://doi.org/10.13140/RG.2.1.4514.1840
[9] Y. Zhuang and Z. Gan. A machine learning approach to enrollment prediction in Chicago Public School. 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS) (2017). doi:10.1109/icsess.2017.8342895
[10] Watkins, A., & Kaplan, A Modeling in R and Weka for Course Enrollment Prediction. (2018)
[11] Wang Y, Liu X, Chen Y Analyzing cross-college course enrollments via contextual graph mining. PLoS ONE 12(11) (2017): e0188577. https://doi.org/10.1371/journal.pone.0188577
[12] Yang, S. &Berdine, G. (2017). The receiver operating characteristic (ROC) curve. The Southwest Respiratory and Critical Care Chronicles. 5. 34. 10.12746/swrccc.v5i19.391.
[13] Slim, A., Hush, D., Ojha, T., & Babbitt, T. Predicting Student Enrollment based on Student and College Characteristics. EDM. (2018).