Enrollment Trends in Philippine Public and Private Basic Education Schools: A Regional Comparative Trend Analysis
Keywords:
Data mining techniques, Enrollment, Gradient Boosting model, Linear Regression model, Random Forest modelAbstract
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.
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