基于神经网络的高校公共选修课课堂教学质量评价方法  

An Evaluation Method for Classroom Teaching Quality of University’s Public Elective Course Based on Neural Network

作　　者：严太山[1] 郭观七[1] 潘理[1] 方欣[1] 

机构地区：[1]湖南理工学院信息与通信工程学院,湖南岳阳414006

出　　处：《湖南理工学院学报（自然科学版）》2013年第2期43-47,共5页Journal of Hunan Institute of Science and Technology(Natural Sciences)

基　　金：湖南理工学院教学改革研究项目(2013B17);湖南省教育厅科研基金(10C0757);湖南省自然科学基金(11JJ2037);湖南省高校科技创新团队(湘教通[2012]318号)支持计划资助

摘　　要：公共选修课是高校课程体系的重要组成部分,公共选修课课堂教学质量评价是高校的一项常规工作,对提高人才培养质量具有重要的意义.本文对高校公共选修课课堂教学质量评价指标体系进行了探讨,针对传统评价方法的不足,将一种二次自适应调整学习参数的改进型BP神经网络算法应用于高校公共选修课课堂教学质量评价之中,提出了一种基于改进型BP算法的高校公共选修课课堂教学质量评价方法,建立了高校公共选修课课堂教学质量评价的神经网络模型.实例表明,该方法能够实现对高校公共选修课课堂教学质量的快速、准确的评价,为提高高校公共选修课课堂教学质量提供了有益的帮助.Public elective course is an important component of university＇s course system. It is a normal and significant work for universities to evaluate classroom teaching quality of public elective course. This paper discusses the evaluation index system of classroom teaching quality of public elective course. Aiming at the limitation of traditional evaluation methods, an improved BP algorithm with two times adaptive adjust of training parameters was applied in the evaluation of classroom teaching quality of public elective course. A neural network model for the classroom teaching quality evaluation of public elective course was founded. The illustrational results show that we can realize a fast and accurate evaluation for classroom teaching quality of public elective course by this method. It is helpful for universities to promote the public elective course quality.

关 键 词：高校公共选修课 课堂教学质量评价 神经网络 BP算法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]