Jie Chen, Min Huang, Bin Chen, Ziwen Sun
Faculty of Continuing Education, Yangzhou Polytechnic Institute, Yangzhou, 225000, China.
College of Social Sciences, Yangzhou University, Yangzhou, 225000, China.
Sci Rep. 2025 Aug 3;15(1):28313. doi: 10.1038/s41598-025-13728-3.
Evaluating the effectiveness of education management requires the integration of multi-source data and information. Based on data modeling technology, combined with data enhancement and transfer learning methods, this paper analyzes the differences in the allocation of education management resources in six universities in different semesters, and systematically explores the actual effectiveness of university education management. By combining data enhancement technology, we expanded the training data, simulated various real-life scenarios, and ensured that the model is more robust to various data changes. This study mainly used two models: simulation-verification model and BP (back propagation) neural network model, and analyzed their management efficiency, prediction accuracy, stability and time cycle. This study proposed two models: simulation-verification model (evaluating the effect by simulating the consistency of management conditions and verification results) and BP neural network model (prediction model based on data enhancement and transfer learning). Experiments show that the BP neural network model is superior to the simulation model in management efficiency (ratio of resource input to actual effect) and stability (volatility of model prediction results), with an average management efficiency of 85.9%, prediction accuracy of 93.1%, and stability of 72.3%. The BP neural network model is superior to the simulation verification model in terms of management efficiency, prediction accuracy, and stability, demonstrating the potential of integrating advanced data processing technologies such as data enhancement and transfer learning into the education management system.
评估教育管理的有效性需要整合多源数据和信息。基于数据建模技术，结合数据增强和迁移学习方法，本文分析了六所大学在不同学期教育管理资源分配的差异，并系统地探究了大学教育管理的实际效果。通过结合数据增强技术，我们扩充了训练数据，模拟了各种现实场景，并确保模型对各种数据变化具有更强的鲁棒性。本研究主要使用了两种模型：模拟验证模型和BP（反向传播）神经网络模型，并分析了它们的管理效率、预测准确性、稳定性和时间周期。本研究提出了两种模型：模拟验证模型（通过模拟管理条件与验证结果的一致性来评估效果）和BP神经网络模型（基于数据增强和迁移学习的预测模型）。实验表明，BP神经网络模型在管理效率（资源投入与实际效果的比率）和稳定性（模型预测结果的波动性）方面优于模拟模型，平均管理效率为85.9%，预测准确率为93.1%，稳定性为72.3%。BP神经网络模型在管理效率、预测准确性和稳定性方面优于模拟验证模型，证明了将数据增强和迁移学习等先进数据处理技术集成到教育管理系统中的潜力。
