摘要
为提高计算机专业教学质量评估准确度,文章提出基于改进BP神经网络的计算机专业教学质量评价方法。首先选取计算机专业教学质量评价影响因素,然后以此为基础,利用改进后的BP神经网络对各影响因素进行分析计算,确定最终的教学质量评价结果。测试结果表明,该评价方法对教学质量评价结果与专家组评价结果之间的误差始终稳定在0.15以内,最小误差仅为0.05,应用效果较好。
To improve the accuracy of teaching quality evaluation in computer science,this paper proposes a computer science teaching quality evaluation method based on an improved BP neural network.Firstly,select the factors that affect the evaluation of teaching quality in the computer major.Then,based on this,use the improved BP neural network to analyze and calculate each influencing factor,and determine the final evaluation result of teaching quality.Design evaluation methods in the test results,and ensure that the error between the teaching quality evaluation results and the expert group evaluation results remains stable within 0.15,with a minimum error of only 0.05.The application effect is good.
作者
赵莉苹
薛丽香
ZHAO Liping;XUE Lixiang(School of Information Engineering,Zhengzhou University of Science and Technology,Zhengzhou 450064,China)
出处
《无线互联科技》
2024年第13期122-124,共3页
Wireless Internet Technology
基金
校级教改项目,项目名称:云平台支持下计算机类专业学生创新能力培养研究,项目编号:2024JGZD12。
关键词
改进BP神经网络
计算机专业
教学质量评价
教学计划
教学手段
教学过程
教学态度
教学内容
教学效果
improved BP neural network
computer major
teaching quality evaluation
teaching plan
teaching means
teaching process
teaching attitude
teaching content
teaching effect
