Improving the Prediction of Student Performance by Integrating a Random Forest Classifier with Meta-Heuristic Optimization Algorithms

Anticipating student performance in higher education is crucial for informed decision-making and the reduction of dropout rates. This study focuses on the intricate analysis of diverse educational datasets using machine learning, particularly emphasizing dimensionality reduction. The aim is to empower educators with data-driven insights, enabling timely interventions for academic improvement. By categorizing individuals based on their inherent aptitudes, the study seeks to mitigate failure rates and enhance the overall educational experience. The integration of predictive modeling, particularly employing the robust Random Forest Classifier (RFC), allows the academic community to proactively address challenges and foster a supportive learning environment, thereby improving student outcomes. To bolster predictive capabilities, the study adopts the RFC model and enhances its efficacy through advanced optimization algorithms, specifically Electric Charged Particles Optimization (ECPO) and Artificial Rabbits Optimization (ARO). These sophisticated algorithms are strategically integrated to refine decision-making processes and enhance predictive precision. Furthermore, the analysis of the input variables has been conducted to assess their individual impact on student performance. This analysis can help institutions identify and address areas for improvement in their management practices. The study's commitment to leveraging state-of-the-art machine learning and bio-inspired algorithms underscores its dedication to achieving precise and resilient predictions of the performance of 4424 students, ultimately contributing to the advancement of educational outcomes. The research outcomes highlight the superiority of the RFEC model, optimized through ECPO for RFC, in aligning with actual measured values, affirming its efficacy in predictive accuracy.

@article{Ma2024,
  title     = {Improving the Prediction of Student Performance by Integrating a Random Forest Classifier with Meta-Heuristic Optimization Algorithms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Chao Ma},
  doi       = {10.14569/IJACSA.2024.01506106},
  url       = {https://doi.org/10.14569/IJACSA.2024.01506106}
}