MAE error values of each model.

With the development of information technology construction in schools, predicting student grades has become a hot area of application in current educational research. Using data mining to analyze the influencing factors of students’ performance and predict their grades can help students identify their shortcomings, optimize teachers’ teaching methods and enable parents to guide their children’s progress. However, there are no models that can achieve satisfactory predictions for education-related public datasets, and most of these weakly correlated factors in the datasets can still adversely affect the predictive effect of the model. To solve this issue and provide effective policy recommendations for the modernization of education, this paper seeks to find the best grade prediction model based on data mining. Firstly, the study uses the Factor Analyze (FA) model to extract features from the original data and achieve dimension reduction. Then, the Bidirectional Gate Recurrent Unit (BiGRU) model and attention mechanism are utilized to predict grades. Lastly, Comparing the prediction results of ablation experiments and other single models, such as linear regression (LR), back propagation neural network (BP), random forest (RF), and Gate Recurrent Unit (GRU), the FA-BiGRU-attention model achieves the best prediction effect and performs equally well in different multi-step predictions. Previously, problems with students’ grades were only detected when they had already appeared. However, the methods presented in this paper enable the prediction of students’ learning in advance and the identification of factors affecting their grades. Therefore, this study has great potential to provide data support for the improvement of educational programs, transform the traditional education industry, and ensure the sustainable development of national talents.

随着学校信息化建设的持续推进，学生成绩预测已成为当前教育研究领域的热门应用方向。利用数据挖掘分析学生学业表现的影响因素并预测其成绩，能够帮助学生认清自身不足、优化教师的教学方法，同时助力家长更好地引导子女成长。但目前针对教育类公开数据集，尚无模型可实现令人满意的预测效果，且数据集中多数弱相关因素仍会对模型的预测性能产生不利影响。为解决这一问题，并为教育现代化建设提供有效政策参考，本文旨在基于数据挖掘方法构建最优的成绩预测模型。首先，本研究采用因子分析（Factor Analyze, FA）模型对原始数据开展特征提取与维度约简；随后，结合双向门控循环单元（Bidirectional Gate Recurrent Unit, BiGRU）模型与注意力机制实现成绩预测；最后，通过消融实验与线性回归（linear regression, LR）、反向传播神经网络（back propagation neural network, BP）、随机森林（random forest, RF）以及门控循环单元（Gate Recurrent Unit, GRU）等单一模型的预测结果对比，验证FA-BiGRU-注意力模型取得了最优的预测效果，且在各类多步预测场景下表现同样优异。此前，学生的学业问题往往仅在出现后才被察觉，而本文提出的方法可提前预判学生的学习状况并识别影响其成绩的核心因素。因此，本研究有望为教育方案优化、传统教育行业转型升级以及保障国家人才可持续发展提供坚实的数据支撑，具备重要的应用价值与发展前景。