学生学习画像的成绩预警问题

Issues of the Early Warning of Academic Records on Student’s Learning Portrait

高校教学的重心在于加强对学生学习成绩的监管并提升学生综合素质与能力。学习成绩受课程开设数量、开设种类、专业难度不同等众多因素的影响,因此为满足实际教学管理的成绩预警的要求,本文利用经典的C45决策树预测分类算法进行求解,采用RBF神经网络模型验证预测结果的准确性,运用MATLAB进行模拟,验证该模型预警的可行性。

The focus of college teaching is to strengthen the supervision of students’ academic performance and improve students’ overall quality and ability. Academic performance is influenced by many factors,such as the number of courses offered,the types of courses offered,and the different difficulty of majors. Therefore,in order to meet the requirements of the achievement early warning of practical teaching management,this paper establishes the classical C45 decision tree prediction classification algorithm to solve,uses the RBF neural network model to verify the accuracy of the prediction results,and employs MATLAB software to simulate,and explains the feasibility of designing the model early warning.