摘要
目前神经网络被广泛应用于高校教学评价,但其具有计算量大及容易陷入局部最优解的缺陷。文章结合变量预测模型分类(VPMCD)方法,提出了基于距离评估技术和VPMCD的高校教师教学质量评价方法。分析结果表明,该方法能有效提高运算效率和预测精度。
At present,neural network is widely used in teaching evaluation of colleges and universities,but it has the shortcomings of large amount of calculation and easy to fall into local optimal solution.Combining the variable predictive model classification (VPMCD) method,this paper proposes a method for evaluating the teaching quality of university teachers based on distance assessment technology and VPMCD.The analysis results show that the method can effectively improve the operational efficiency and prediction accuracy.
作者
彭延峰
刘燕飞
何宽芳
PENG Yan-feng;LIU Yan-fei;HE Kuan-fang(Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment,Hunan University ofScience and Technology,Xiangtan,Hunan 411201,China;Engineering Research Center of Advanced Mining Equipment,Ministry of Education,Hunan University ofScience and Technology,Xiangtan,Hunan 411201,China;School of Mechanical Engineering,Hunan University of Science and Technology,Xiangtan,Hunan 411201,China)
出处
《教育教学论坛》
2019年第37期69-72,共4页
Education And Teaching Forum
基金
国家重点研发项目子课题（2018YFF0212902）
国家自然科学基金项目（51805161）
湖南省自然科学基金青年项目（2018JJ3187）
2015年湖南省普通高等学校教学改革研究项目（湘教通[2015]291号）
关键词
教育改革
教学质量评价
距离评估技术
变量预测模型分类
educational reform
teaching quality evaluation
variable predictive model based class discriminate
distance evaluation technique
