摘要
农村教师培训是新形势下践行农村基础教育精准扶贫的重要手段。本文以福建省6个县18所农村学校435名教师的调研数据为基础,运用有序多分类Logistic回归模型,实证分析教师个人背景因素、家庭因素、学校因素、培训因素及地区因素等五个方面因素对农村教师培训需求意愿的影响。研究表明,在整体上农村教师培训需求意愿比较强烈。年龄、文化程度、职称、是否有编制、月收入、家庭人口数、学校地理位置、是否有培训经历、培训师资、培训方式、培训作用、地区等变量对农村教师培训需求意愿有显著影响。增加培训前后期的调查,组建专家团队,创新培训机制,加强教师发展机构建设,加快推进农村教育信息化建设,有助于全方位推进农村教育精准扶贫,进而促进农村教育质量的全面提升。
Rural teacher training is an important means to implement targeted poverty alleviation of rural basic education in the new situation.Based on the field survey data of 435 teachers from 18 rural schools in 6 counties of Fujian Province,an ordered multiclassification logistic regression model is used to empirically analyze the influence of five factors,including teachers' personal background factors,family factors,school factors,training factors and regional factors on rural teachers' training demand willingness.Research shows that:On the whole,the rural teachers' training demand willingness is relatively strong.Variables such as age,educational level,professional title,whether to have teacher establishment,monthly income,family population,school geographical location,training experience,training teachers,training methods,training role,region have a significant impact on rural teachers training demand willingness.Increasing the investigation before and after the training,setting up the expert teams,innovating the training mechanism,strengthening the construction of teacher development institutions,and accelerating the construction of rural education informatization are conducive to promoting the targeted poverty alleviation of rural education in an all-round way,so as to promote the overall improvement of rural education quality.
作者
肖庆业
XIAO Qing-ye(School of Business,Minnan Normal University,Zhangzhou,Fujian,363000,China)
出处
《基础教育》
北大核心
2020年第6期21-32,共12页
Journal of Schooling Studies
基金
福建省社会科学规划项目《基于教师内在需求的福建省农村义务教育教师培训调查研究》(项目编号:FJ2016B134)
福建省教育厅“商务大数据分析与应用福建省高校重点实验室”建设项目(闽教科[2017]115号)。
关键词
农村教师
培训需求意愿
影响因素
有序多分类Logistic回归模型
rural teachers
training demand willingness
influencing factors
ordered multiclassification logistic regression model