BP神经网络在研究生课堂教学评价中的应用

为解决现阶段研究生课堂教学监测和实施策略上的不足,该文提出一种利用BP神经网络机器学习动态评价研究生课堂教学质量的方法。该方法能够通过机器学习减少对专家的依赖,从而节省教学评估所需的人力和物力资源,确保课堂教学评价的准确性和稳定性。该文详细阐述BP神经网络在研究生课堂教学质量评价中的实践措施,包括评价指标体系构建、数据采集与处理、BP神经网络模型设计与优化、实际应用与评估和持续改进与推广。该文实现教学监测与神经网络领域的跨学科融合,为研究生教育质量监测改革带来新的思路和方法。

To address the current shortcomings in monitoring and implementing strategies for graduate classroom teaching,this paper proposes a method using BP neural network machine learning to dynamically evaluate the quality of graduate classroom teaching.The BP neural network method can reduce dependence on experts through learning,thereby saving human and material resources required for teaching evaluation,while ensuring the accuracy and stability of classroom teaching evaluation.This paper elaborates the practical measures of using the BP neural network in evaluating the quality of postgraduate classroom teaching,including the construction of an evaluation index system,data collection and processing,BP neural network model design and optimization,practical application and assessment,and continuous improvement and promotion.This study achieves interdisciplinary integration between teaching monitoring and the field of neural networks,bringing new ideas and methods for the reform of graduate education quality monitoring.