Predicting Student Achievement Using Socioeconomic and School-Level Factors

Abstract

This study compares the performance of two machine learning models, Random Forest (RF) and XGBoost, in predicting student achievement based on socioeconomic and school-level factors. Both models demonstrated exceptional performance, with XGBoost slightly outperforming RF across key metrics, including accuracy, precision, and recall. The analysis revealed that socioeconomic factors, such as family income and parental education levels, as well as school characteristics, were the most significant predictors of student success. These findings align with the broader literature, reinforcing the influence of external factors on educational outcomes. The implications of this study suggest that educational policies should focus on addressing socioeconomic disparities to improve student performance. Schools serving disadvantaged communities would benefit from increased access to resources, academic support, and parental engagement programs. The importance of these factors in shaping student outcomes points to the need for equitable resource distribution, with targeted interventions aimed at closing the achievement gap. For future research, the application of deep learning models and hybrid approaches could enhance predictive accuracy and provide further insights into student performance. Additionally, incorporating more granular data at the student level, such as individual academic progress and behavioral metrics, may improve model precision. Exploring other datasets with varying socioeconomic contexts could also extend the generalizability of these findings. In conclusion, while RF and XGBoost both performed well, the findings underscore the need for continuous improvements in model development. Integrating more advanced features, such as longitudinal tracking of socioeconomic variables, would offer a more dynamic understanding of how these factors evolve and impact educational success. These insights could guide more effective interventions, ultimately fostering a more equitable educational environment.