IJIET 2025 Vol.15(9): 1864-1872
doi: 10.18178/ijiet.2025.15.9.2387
doi: 10.18178/ijiet.2025.15.9.2387
Engineering Students’ Performance Prediction on Board Examination Using Classification Algorithms
Jayson A. Batoon1,* and Sarah Jane L. Cabral2
1. College of Information and Communications Technology, Faculty, Bulacan State University, Malolos, Philippines
2. Information Technology Department, College of Engineering Eastern Visayas State University, Tacloban City Leyte, Philippines
Email: jasyon.batoon@bulsu.edu.ph (J.A.B.); sarahjane.cabral@evsu.edu.ph (S.J.L.C.)
*Corresponding author
2. Information Technology Department, College of Engineering Eastern Visayas State University, Tacloban City Leyte, Philippines
Email: jasyon.batoon@bulsu.edu.ph (J.A.B.); sarahjane.cabral@evsu.edu.ph (S.J.L.C.)
*Corresponding author
Manuscript received March 12, 2025; revised April 24, 2025; accepted May 15, 2025; published September 11, 2025
Abstract—Board examinations are critical assessments that determine the academic and professional readiness of engineering students. Accurately predicting board exam outcomes can support timely interventions, helping institutions and educators enhance student preparedness. This study developed a predictive model using machine learning classification algorithms, specifically logistic regression, decision trees, random forest, and Naïve Bayes, to forecast the board examination performance of engineering students based on academic and preparatory indicators such as general weighted average, pre-board scores, and review center participation. Among the models tested, logistic regression achieved the highest accuracy (66.7%), closely followed by Naïve Bayes (66.1%). The findings emphasize the predictive value of pre-board performance and institutional review programs. This research highlights how predictive analytics can improve educational strategies and support systems, ultimately aiming to raise board exam success rates. Future research is encouraged to integrate additional variables, including psychological and behavioral factors, to further enhance model accuracy.
Keywords—board exam prediction, machine learning, classification algorithm
Copyright © 2025 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).
Keywords—board exam prediction, machine learning, classification algorithm
Cite: Jayson A. Batoon and Sarah Jane L. Cabral, "Engineering Students’ Performance Prediction on Board Examination Using Classification Algorithms," International Journal of Information and Education Technology, vol. 15, no. 9, pp. 1864-1872, 2025.
Copyright © 2025 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).