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<p>Academic achievement is vital for campus life and education since it indicates the caliber of the teachers, administration, and students’ learning abilities. Issues such as poor study conditions and family disruptions can impede a student’s capacity to achieve. Teachers are looking for practical solutions to these concerns because solving problems one at a time might be tough. This study uses a combination of black hole optimization (BHO) and Gaussian process regression (GPR) algorithms to predict students’ academic success in higher education. The method is divided into three stages: data pre-processing, identification of effective indicators using BHO algorithms, and forecasting of academic performance. The presented approach makes use of the GPR algorithm to choose the relevant features and the weighted combination of GPR models to forecast that the GPR model would be used for the weighting operation that is, to determine the ideal weights. The experimental findings demonstrate that our method has a lower error rate of 0.95 and 0.81 in terms of RMSE and MAE than the competing methods. The proposed method can assist teachers in analyzing student behavioral patterns, understanding academic performance impact mechanisms, and developing effective learning supervision plans.</p>

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Predicting the academic achievement of students using black hole optimization and Gaussian process regression

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月28日

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  • Yanyu Chen,
  • Xiaolin Yao

摘要

Academic achievement is vital for campus life and education since it indicates the caliber of the teachers, administration, and students’ learning abilities. Issues such as poor study conditions and family disruptions can impede a student’s capacity to achieve. Teachers are looking for practical solutions to these concerns because solving problems one at a time might be tough. This study uses a combination of black hole optimization (BHO) and Gaussian process regression (GPR) algorithms to predict students’ academic success in higher education. The method is divided into three stages: data pre-processing, identification of effective indicators using BHO algorithms, and forecasting of academic performance. The presented approach makes use of the GPR algorithm to choose the relevant features and the weighted combination of GPR models to forecast that the GPR model would be used for the weighting operation that is, to determine the ideal weights. The experimental findings demonstrate that our method has a lower error rate of 0.95 and 0.81 in terms of RMSE and MAE than the competing methods. The proposed method can assist teachers in analyzing student behavioral patterns, understanding academic performance impact mechanisms, and developing effective learning supervision plans.