基于SSA-SVR的大学公共英语教学质量评价  

Evaluation of College English Teaching Quality Based on SSA-SVR

作　　者：仵宁[1] 孙瑞[1] 苏琴[1] Su Ning;Sun Rui;Su Qin(Xi'an Innovation College,Yan'an University,Xi'an 710000,Shaanxi)

机构地区：[1]延安大学西安创新学院,陕西西安710000

出　　处：《现代科学仪器》2020年第1期136-140,共5页Modern Scientific Instruments

摘　　要：为提高大学公共英语教学质量评价的精度,借鉴平衡记分卡法从教学态度、教学内容、教学艺术、课堂结构、课堂管理和教学效果构建大学公共英语教学质量评价指标体系。针对支持向量回归模型性能受其惩罚参数C和核参数g的选择影响,将樽海鞘算法应用于惩罚参数C和核参数g的优化选择,提出一种大学公共英语教学质量的SSA-SVR评价模型。将20个大学公共英语教学质量评价的二级指标作为SSA-SVR模型的输入,大学公共英语教学质量评价综合得分作为SSA-SVR的输出,建立大学公共英语教学质量的SSA-SVR评价模型。研究结果表明,与GA-SVR、PSO-SVR、DE-SVR和SVR相比,SSA-SVR的大学公共英语教学质量评价精度最高,为大学公共英语教学质量评价提供了新的途径和方法。In order to improve the accuracy of college English teaching quality evaluation,the balanced scorecard method is used to build an index system of college English teaching quality evaluation from teaching attitude,teaching content,teaching art,classroom structure,classroom management,and teaching effect.As the performance of support vector regression model is affected by the choice of its penalty parameter C and kernel parameter g,a bottle sea scabbard algorithm is used to optimize the choice of penalty parameter C and kernel parameter g.An SSA-SVR evaluation model for the quality of public English teaching in universities.The secondary indicators of the quality evaluation of 20 college English teaching are used as the input of the SSA-SVR model,and the comprehensive score of the quality evaluation of college public English teaching is used as the output of the SSA-SVR.The results show that compared with GA-SVR,PSO-SVR,DE-SVR,and SVR,SSA-SVR has the highest accuracy in the evaluation of college English teaching quality,and provides a new approach and method for the evaluation of college English teaching quality.

关 键 词：支持向量回归 大学公共英语 教学质量评价 樽海鞘算法 评价指标体系 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]