| 职业农民新技术培训效果:概念框架、影响因素与对策建议 |
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| 引用本文： | 吕雅辉,李含悦,张亮,白丽,张润清.职业农民新技术培训效果:概念框架、影响因素与对策建议[J].职业技术教育,2020(10):62-67. |
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| 作者姓名： | 吕雅辉 李含悦 张亮 白丽 张润清 |
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| 作者单位： | 河北农业大学经济管理学院 |
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| 基金项目： | 河北省社科基金项目“河北省黑龙港流域‘菜改菌’实施方案与政策措施研究”(HB16YJ061),主持人:白丽。 |
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| 摘 要： | 基于培训迁移理论,构建了培训效果影响的概念框架。以湖北、山东、安徽3省5市10县832份职业农民问卷调查数据为基础,构建培训环境因素、动机因素、系统因素、前涉因素对培训效果影响的结构方程模型,研究结果显示,培训环境因素、动机因素、系统因素、前涉因素均对培训效果具有正向影响,且影响的贡献率依次降低。这表明培训系统因素与前涉因素对培训效果的影响低于培训环境因素和培训动机因素,即政府主导下的农民教育培训本身对迁移效果影响不显著。
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| 关 键 词： | 职业农民 新技术培训 培训效果 结构方程模型 影响因素 |
Training Effects of New Technologies for Professional Farmers:Conceptual Framework,Influencing Factors and Countermeasures |
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| Lv Yahui,Li Hanyue,Zhang Liang,Bai Li,Zhang Runqing.Training Effects of New Technologies for Professional Farmers:Conceptual Framework,Influencing Factors and Countermeasures[J].Vocational and Technical Education,2020(10):62-67. |
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| Authors: | Lv Yahui Li Hanyue Zhang Liang Bai Li Zhang Runqing |
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| Institution: | (Hebei Agricultural University,Baoding 071000) |
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| Abstract: | Based on the theory of training transfer,a conceptual framework for the impact of training input factors,is constructed.At the same time,based on the questionnaire survey data of 832 professional farmers in 5 cities and 10 counties of Hubei,Shandong,and Anhui,a structural equation model of the effects about training environmental factors,motivation factors,system factors,and antecedent factors on training effects is constructed.The research results show that the training environment factors,motivation factors,system factors,and previous factors all have positive impacts on the training effects,and the contribution rate of the impact decreases in order.This shows that the impact of training system factors and anteceden factors on training effects is lower than the training environment factors and training motivation factors,that is,the government-led farmers'education training itself has no significant effect on the migration effect. |
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| Keywords: | professional farmers new technology training training effects structural equation model influencing factors |
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