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Machine learning techniques to predict student performance using a dataset of demographic, behavioral, and academic indicators are being used extensively in recent research. Using algorithms like support vector machines, random forests, and neural networks, the random forest algorithm achieves the highest predictive accuracy. The study provides insights into factors influencing student performance, such as engagement and academic achievement. It offers practical implications for educators and policymakers, enabling targeted interventions to improve learning outcomes.

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Mathematical Modeling of Student Performance Prediction Using Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • S. Dilli Babu,
  • C. Nadhamuni Reddy,
  • Errapaneni Gayatri,
  • Sai Nomitha Yarabolu,
  • A. V. Sriharsha,
  • M. Sunil Kumar,
  • D. Ganesh,
  • Kuraku Nirmala

摘要

Machine learning techniques to predict student performance using a dataset of demographic, behavioral, and academic indicators are being used extensively in recent research. Using algorithms like support vector machines, random forests, and neural networks, the random forest algorithm achieves the highest predictive accuracy. The study provides insights into factors influencing student performance, such as engagement and academic achievement. It offers practical implications for educators and policymakers, enabling targeted interventions to improve learning outcomes.