Mahawar Kajal, Rattan Punam, Jalamneh Ammar, Ab Yajid Mohd Shukri, Abdeljaber Omar, Kumar Raman, Lasisi Ayodele, Ammarullah Muhammad Imam
Lovely Professional University, Phagwara, 144411, Punjab, India.
College of Arts and Science, Applied Science University, Al Ekir, 3201, Kingdom of Bahrain.
Sci Rep. 2025 Mar 24;15(1):10172. doi: 10.1038/s41598-025-94642-6.
Higher education is essential because it exposes students to a variety of areas. The academic performance of IT students is crucial and might fail if it isn't documented to identify the features influencing them, as well as their strengths and shortcomings. The student academic prediction system needs to be enhanced so that teachers can forecast their students' performance. Numerous studies have been conducted to increase the prediction accuracy of IT students, but they encountered difficulties with unbalanced data and algorithm tuning. To address these issues, the study proposed different machine learning (ML) algorithms that handled imbalanced data by applying the synthetic minority oversampling technique (SMOTE) and employing hyperparameter tuning algorithms to enhance prediction during the training process. The ML models we used were decision tree (DT), k-nearest neighbor, and XGBoost. The models were fine-tuned by applying Ant colony optimization (ACO) and artificial bee colony optimization techniques. Subsequently, these optimization techniques further enhanced the performance of the models. After comparing them, the results showed that SMOTE and ACO combined with the DT model outperformed other models for academic prediction. Additionally, the study utilized the Kendall Tau correlation coefficient technique to analyze the correlation between features and identify factors that positively or negatively impact student success.
高等教育至关重要，因为它能让学生接触到各个领域。信息技术专业学生的学业表现至关重要，如果不记录影响他们的特征以及他们的优点和缺点，学业表现可能会失败。学生学业预测系统需要改进，以便教师能够预测学生的表现。已经进行了大量研究以提高信息技术专业学生的预测准确性，但他们在处理不平衡数据和算法调优方面遇到了困难。为了解决这些问题，该研究提出了不同的机器学习（ML）算法，这些算法通过应用合成少数过采样技术（SMOTE）来处理不平衡数据，并采用超参数调优算法在训练过程中提高预测能力。我们使用的ML模型有决策树（DT）、k近邻和XGBoost。通过应用蚁群优化（ACO）和人工蜂群优化技术对模型进行了微调。随后，这些优化技术进一步提高了模型的性能。经过比较，结果表明，SMOTE和ACO与DT模型相结合在学业预测方面优于其他模型。此外，该研究利用肯德尔等级相关系数技术分析特征之间的相关性，并确定对学生学业成功有正面或负面影响的因素。
