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This research paper developed a robust neural network (ANN) model in predicted academic performance by correlation between academic success and psychological, emotional, behavioral, and environmental variables. The suggested method used is Artificial Neural Networks (ANN) for prediction after extensive use of data pre-processing and exploratory data analysis to guarantee data quality. In this paper, clustering techniques and hyperparameter modification are used to further improve the accuracy of the ANN model. The R2 score of 0.74 and a mean error (MAE) of 0.176, this method has strong predictive power. This study emphasizes how importance of optimization and prioritization are generating accurate estimates. The results demonstrate how well ANN analyzes a variety of dataset and provides insightful evaluations of student performance. In conclusion, when combination with comprehensive data and optimization, ANN methods provide powerful tools for enhancing learning outcomes in complex learning environments.

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Student Academic Performance Evaluation and Prediction Using Optimized Artificial Neural Network

  • 会议论文

  • 发表日期：

    2026年

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  • Hiradi Lazarus Michael,
  • Rajesh Prasad,
  • Olumide Owolabi,
  • Birendra Kumar Sharma

摘要

This research paper developed a robust neural network (ANN) model in predicted academic performance by correlation between academic success and psychological, emotional, behavioral, and environmental variables. The suggested method used is Artificial Neural Networks (ANN) for prediction after extensive use of data pre-processing and exploratory data analysis to guarantee data quality. In this paper, clustering techniques and hyperparameter modification are used to further improve the accuracy of the ANN model. The R2 score of 0.74 and a mean error (MAE) of 0.176, this method has strong predictive power. This study emphasizes how importance of optimization and prioritization are generating accurate estimates. The results demonstrate how well ANN analyzes a variety of dataset and provides insightful evaluations of student performance. In conclusion, when combination with comprehensive data and optimization, ANN methods provide powerful tools for enhancing learning outcomes in complex learning environments.