Home⇛Journal of Interdisciplinary Perspectives⇛vol. 4 no. 5 (2026)

Enrollment Trends in Philippine Public and Private Basic Education Schools: A Regional Comparative Trend Analysis

Philip John L. Paja

Discipline: Education

 

Abstract:

This study looks at enrollment trends in Philippine public and private basic education schools across different regions. It examines trends over ten academic years, from SY 2010-2011 to SY 2020-2021. The study aims to find the regions with the highest and lowest enrollment and to estimate the average annual growth rate of student enrollment. It addresses the need for a broad regional analysis that was missing from earlier research and provides data-based insights for educational planning. The dataset comes from the Department of Education (DepEd) and includes three main variables: academic year, educational sector (public or private), and administrative region. To look at trends and predict future enrollment, the study uses Random Forest, Gradient Boosting Machine (GBM), and Linear Regression models. These models were chosen because they can manage complex patterns and give reliable predictions. The Random Forest model did better than both Linear Regression and GBM, achieving an R² of 0.98 and a low RMSE for students in basic education. This shows how effective it is at identifying enrollment trends. The findings presented that Region IV-A (CALABARZON) has the highest enrollment, while CAR and BARMM have the lowest. The trends showed changes over the years, identifying our regional differences, and these results provide valuable information for policymakers and education planners. They can use this to make better decisions about resource distribution, program development, and education strategies in various regions. The study shows that data-driven analysis can clearly show enrollment trends and support data-based policy in the Philippines' basic education system.



References:

  1. Albreiki, B., Zaki, N., & Alashwal, H. (2021). A systematic literature review of student performance prediction using machine learning techniques. Education Sciences, 11(9), 552. https://doi.org/10.3390/educsci11090552
  2. Bishop, C. (2006). Pattern recognition and machine learning. New York, NY: Springer. https://link.springer.com/book/9780387310732
  3. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  4. Dela Cruz, A., Reyes, M.L., Santos, J.P., & Gomez, R.T. (2020). Higher education institution enrollment forecasting using data mining techniques. International Journal of Advanced Trends in Computer Science and Engineering, 9(2), 2060–2064. https://doi.org/10.30534/ijatcse/2020/179922020
  5. Delima, A.J., Sison, A., & Medina, R. (2019). Variable reduction-based prediction through a modified genetic algorithm. International Journal of Advanced Computer Science and Applications, 10(5), 356–363. https://doi.org/10.14569/IJACSA.2019.0100544
  6. Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems (2nd ed.). O’Reilly Media.
  7. Haris, N.A., Abdullah, M., Hasim, N., & Rahman, F.A. (2016). A study on students’ enrollment prediction using data mining. In D. Taniar, M. H. Böhlen, & J. W. Rahayu (Eds.), Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication (pp. 1–6). Danang, Vietnam: ACM. https://doi.org/10.1145/2857546.2857633
  8. Hyndman, R., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). Melbourne, Australia: OTexts. https://otexts.com/fpp3
  9. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: With applications in R (2nd ed.). New York, NY: Springer. https://doi.org/10.1007/978-1-0716-1418-1
  10. Parveen, K., Shah, N.H., & Mahmood, Z. (2020). Evaluation of enrollment trends in technological subjects at the secondary level in Punjab. Global Social Sciences Review, 5(1), 538–550. https://doi.org/10.31703/gssr.2020(V-I).55
  11. Rabelo, A., Rodrigues, M., Nobre, C., Isotani, S., & Zárate, L. (2024). Educational data mining and learning analytics: A review of educational management in e-learning. Information Discovery and Delivery, 52(2), 149–163. https://doi.org/10.1108/IDD-10-2022-0099
  12. Raiblaze. (2022). Philippines school enrollment data [Dataset]. Kaggle. https://tinyurl.com/4nt24sdp
  13. Roman, A.G., & Villanueva, R.U. (2018). Enrolment trend analysis among transition periods of a university for management intervention. International Journal of Science and Research, 7(10), 604–608.
  14. Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355. https://doi.org/10.1002/widm.1355
  15. Sarker, I. (2021). Machine learning: Algorithms, real-world applications, and research directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x
  16. Sharma, R., & Kumar, P. (2022). Forecasting student enrollment using ensemble learning techniques. Expert Systems with Applications, 195, 116595. https://doi.org/10.1016/j.eswa.2022.116595
  17. Tan, E. (2017). Quality, inequality, and recent education reform. Philippine Review of Economics, 54(2), 110–137. https://tinyurl.com/yzab626w
  18. Wuttaphan, N. (2017). Human capital theory: The theory of human resource development, implications, and future. Social Sciences, 18(2), 240–253
  19. Zhou, Z.-H. (2021). Machine learning. Springer Singapore.

Tools


Copyright © 2026  KITE Digital Educational Solutions |  Exclusively distributed by CE-Logic