基于神经网络的学生成绩预测与分析方法

Method for Predicticting and Analyzing Students′Achievement Based on Neural Network

学生成绩是学校教务部门和学工部门管理与引导学生的重要指标,也是进行课程改革的重要依据之一。针对学校缺乏有效的定量与预测问题,提出成绩分析与基于神经网络的预测方法。通过大数据分析成绩影响因素,从现有的学习成绩和学生的其他相关信息中找出对成绩影响较大的因素及其关联关系,并采用BP神经网络预测成绩,实验证明,该模型能准确预测学生的升学成绩。测试结果表明,该模型能有效弥补传统的成绩定性预测的不足,具有较高的预测精度及实际应用价值。

The students′achievement is an important indicator of the management and guidance of students by the educational administration department and the academic engineering department,and it is also one of the important basis for curriculum reform.In view of the lack of effective quantitative analysis and prediction in school,the paper puts forward the method of achievement analysis and prediction based on neural network.Through the big data analysis of the factors affecting the achievement,the factors that have greater impact on the achievement are found out from the academic achievement and other relevant information of students,and BP neural network is used to achieve achievement prediction.The result shows that the model can predict the students′academic achievement accurately,overcome the shortcomings of traditional qualitative achievement prediction effectively,and retains the well practical application value.