Embedding Naïve Bayes Algorithm Data Model in Predicting Student Graduation
Proceedings of the 3rd International Conference on Telecommunications and Communication Engineering
(2019), pp. 51-56
Ace C. Lagman
a
,
Joseph Q. Calleja
a
,
Ma. Corazon G. Fernando
a
,
Joseph G. Gonzales
a
,
John Benedict C. Legaspi
a
,
John Heland Jasper C. Ortega
a
,
Ronel F. Ramos
a
,
Maria Vicky S. Solomo
a
,
Regina C. Santos
a
a FEU Institute of Technology, Manila, Philippines
Abstract: In the Philippines, according to Philippine Authority of Statistics, there is an imbalance between the student enrollment and student graduation. Almost half of the first-time freshmen full time students who began seeking a bachelor's degree do not graduate on time. The study aims to utilize how Naïve Bayes algorithm - a data classification algorithm that is based on probabilistic analysis - can be used in educational data mining specifically in student graduation. The study is focused on the application of the Naïve Bayes algorithm in predicting student graduation by generating a model that could early predict and identify students who are prone of not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions.