基于SSA-BP算法的高校教育管理质量评价模型研究  

Construction of Quality Evaluation Model for Educational Management in Colleges and Universities Based on SSA-BP Algorithm

作　　者：王丽佳 张文台 Wang Lijia;Zhang Wentai(College of Art Design of Shanghai Jian Qiao University,Shanghai,201306)

机构地区：[1]上海建桥学院艺术设计学院,上海201306

出　　处：《现代科学仪器》2024年第4期239-244,共6页Modern Scientific Instruments

基　　金：2019年度肇庆学院学生事务管理精品项目:供给侧结构改革背景新时代大学生就业能力培养及就业平台建设(ZQJPZD-2019009)。

摘　　要：高校教育管理质量是保证高校教育质量的重要环节,对其进行评估能够为学校教育发展决策与方向提供参考,同时能够监测当前教育方向和决策的具体效益。研究结合麻雀搜索算法改进反向传播神经网络,以提高质量评估的准确性和可靠性,由此提出了一种新型的高校教育管理质量评价模型。实验表明,经过优化后的神经网络在平均绝对误差为3.4907,均方误差根为4.4245,平均绝对百分比误差为0.77%,预测准确率为99.23%,算法运行时间为214.13s。模型期望输出值与实际输出值之间准确度误差不过1.5%,可见此次研究的质量评估模型具有良好的评估能力,对高校教育管理决策提供了一定的依据。The quality of university education management is an important link in ensuring the quality of university education.Assessing it can provide reference for decision-making and direction of school education development,while also monitoring the specific benefits of current education direction and decision-making.A new model for evaluating the quality of university education management is proposed by combining sparrow search algorithm to improve BP neural network to improve the accuracy and reliability of quality assessment.The experiment shows that the optimized neural network has an average absolute error of 3.4907,a root mean square error of 4.4245,an average absolute percentage error of 0.77%,a prediction accuracy of 99.23%,and an algorithm running time of 214.13 seconds.The accuracy error between the expected output value of the model and the actual output value is only 1.5%,indicating that the quality evaluation model in this study has good evaluation ability and provides a certain basis for decision-making in university education management.

关 键 词：麻雀搜索算法 BP神经网络 高校教学质量 评价模型 

分 类 号：G642[文化科学—高等教育学]