基于主成分分析法的学生英语学习倦怠影响因素研究  

Research on the Factors Influencing Students'Burnout in English Learning Based on Principal Component Analysis

作　　者：王婧锦[1] Wang Jingjin(Shaanxi University of Traditional Chinese Medicine,Xianyang 712046,Shaanxi)

机构地区：[1]陕西中医药大学,陕西咸阳712046

出　　处：《现代科学仪器》2020年第3期152-155,共4页Modern Scientific Instruments

摘　　要：大学生普遍存在英语学习倦怠现状,本文采用主成分分析法对大学生英语学习倦怠影响因素进行了研究。给出了大学生英语学习倦怠的九个因素,即学习环境、课程考核、课程内容、师资力量、课程认知、学习习惯、课程基础、学习情绪、教学方式,同时通过主成分分析提取了方差,在总方差中比重超过90%的前四个主成分。借助BP与PCA联合神经网络对大学生英语学习倦怠进行识别,验证了BP与PCA联合神经网络相对于BP神经网络在大学生英语学习倦怠的识别准确率和识别效率上都有了很大程度的提升。The current status of English learning burnout among college students is widespread.This article uses principal component analysis to study the influencing factors of college students'English learning burnout.Nine factors of college students'English learning burnout are given,namely learning environment,curriculum assessment,curriculum content,teacher strength,curriculum cognition,learning habits,curriculum basis,learning emotions,teaching methods,and the variance is extracted through principal component analysis.Four principal components total variance exceeds 90%.With the help of BP and PCA joint neural network to identify college students'English learning burnout,it is verified that BP and PCA joint neural network has improved the recognition accuracy and efficiency of college students'English learning burnout compared to BP neural network.

关 键 词：主成分分析 英语学习倦怠 BP神经网络 

分 类 号：G642[文化科学—高等教育学]