Tang Bo, Li Senlin, Zhao Changhua
School of Computer and Artificial Intelligence, Huaihua University, Huaihua 418000, China.
J Intell. 2024 Dec 3;12(12):124. doi: 10.3390/jintelligence12120124.
Universities and schools rely heavily on the ability to forecast student performance, as it enables them to develop efficient strategies for enhancing academic results and averting student attrition. The automation of processes and the management of large datasets generated by technology-enhanced learning tools can facilitate the analysis and processing of these data, which provides crucial insights into the knowledge of students and their engagement with academic endeavors. The method under consideration aims to forecast the academic achievement of students through an ensemble of deep neural networks. The proposed method presents a new feature-ranking mechanism based on existing approaches. This mechanism is effective in identifying the most pertinent features and their correlation with the academic performance of students. The proposed method employs an optimization strategy to concurrently configure and train the deep neural networks within our ensemble system. Furthermore, the proposed ensemble model uses weighted voting among its learning components for more accurate prediction. Put simply, the suggested approach enhances the accuracy of academic performance predictions for students not only by employing weighted ensemble techniques, but also by optimizing the parameters of deep learning models. These experimental outcomes provide evidence that the proposed method outperformed the alternative approaches, accurately predicting student performance with a root-mean-square error (RMSE) value of 1.66, a Mean Absolute Percentage Error (MAPE) value of 9.75, and an R-squared value of 0.7430. These results show a significant improvement compared to the null model (RMSE = 4.05, MAPE = 24.89, and R-squared = 0.2897) and prove the efficiency of the techniques employed in the proposed method.
大学和学校严重依赖预测学生成绩的能力，因为这使它们能够制定提高学术成绩和避免学生流失的有效策略。流程自动化以及对技术增强学习工具生成的大型数据集的管理，可以促进这些数据的分析和处理，从而提供有关学生知识及其参与学术活动情况的关键见解。所考虑的方法旨在通过深度神经网络的集成来预测学生的学业成绩。该方法基于现有方法提出了一种新的特征排序机制。这种机制能有效地识别最相关的特征及其与学生学业成绩的相关性。该方法采用一种优化策略来同时配置和训练集成系统中的深度神经网络。此外，所提出的集成模型在其学习组件之间使用加权投票以进行更准确的预测。简而言之，所建议的方法不仅通过采用加权集成技术，而且通过优化深度学习模型的参数，提高了对学生学业成绩预测的准确性。这些实验结果表明，所提出的方法优于其他方法，以均方根误差（RMSE）值1.66、平均绝对百分比误差（MAPE）值9.75和R平方值0.7430准确预测了学生成绩。与零模型（RMSE = 4.05，MAPE = 24.89，R平方 = 0.2897）相比，这些结果有显著改善，并证明了所提出方法中使用的技术的有效性。
