Abstract—According to National Center for Education Statistics, almost half of the first-time freshmen full time students who began seeking a bachelor’s degree do not graduate. The imbalance between the student enrolment and student graduation can be solved by early predicting and identifying students who are prone of not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions. The study focused on the application of the ensemble models in predicting student graduation. Ensemble modeling is the process of running two or more related but different analytical models and then synthesizing the results into a single score or spread in order to improve the accuracy of predictive analytics and data mining applications. The study recorded an increase of classification accuracy in predicting student graduation using ensemble models and combining multiple algorithms.
Index Terms—Machine learning, ensemble model, student graduation, predictive analytics.
Ace C. Lagman, Lourwel P. Alfonso, Marie Luvett I. Goh, Jay-ar P. Lalata, Juan Paulo H. Magcuyao, and Heintjie N. Vicente are with the FEU Institute of Technology, P. Paredes St. Sampaloc, Manila, Philippines (e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com).
Cite:Ace C. Lagman, Lourwel P. Alfonso, Marie Luvett I. Goh, Jay-ar P. Lalata, Juan Paulo H. Magcuyao, and Heintjie N. Vicente, "Classification Algorithm Accuracy Improvement for Student Graduation Prediction Using Ensemble Model," International Journal of Information and Education Technology vol. 10, no. 10, pp. 723-727, 2020.Copyright © 2020 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).