Abstract—Almost all major colleges and state universities assess students’ comprehensive quality and set different rewards and regulations for the various level in order to stimulate students’ interest to study and participate in extracurricular activities. The main reward system that is used is the providing of financial incentives such as scholarship grants. In this paper, several data mining techniques such as clustering and forecasting was integrated to discover and assess future outcomes and matters concerning scholarship offerings in SSCT. The Student Financial Assistance Unit (SFAU) of Surigao State College of Technology (SSCT) holds all the records of scholarship grants and its grantees from June 2014. The study visualizes the increase of the grantees in every scholarship grants in the next five years to prepare the budget that needs to be allocated by the sponsoring agents. ARIMA(1,0,0) model for time series analysis was used in the study and found out to be very effective as it produced results as shown in Fig. 11-23.
Index Terms—Scholarship prediction, scholarship grants, kmeans, arima, trend analysis.
A. J. Delima is with the College of Engineering and Information Technology, Surigao State College of Technology, Surigao City, Philippines (e-mail: firstname.lastname@example.org).
Cite: Allemar Jhone P. Delima, "Predicting Scholarship Grants Using Data Mining Techniques," International Journal of Machine Learning and Computing vol. 9, no. 4, pp. 513-519, 2019.Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).