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基于CIPP-AHP和神经网络的高职院校专业课程思政教学评价 被引量:2

Evaluation of Ideological and Political Teaching of Higher Vocational Professional Courses Based on CIPP-AHP and Neural Network
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摘要 在分析高职院校专业课程思政教学影响因素的基础上,通过CIPP评价理论构建包含专业课程思政背景、专业课程思政投入、专业课程思政实施、专业课程思政效果4个方面36个三级指标的高职院校专业课程思政教学评价指标体系,利用AHP(层次分析法)确定各评价指标的重要性和权重,采取BP(反向传播)神经网络建立高职院校专业课程思政教学评价模型。选取75组高职院校专业课程思政教学专家评价数据作为训练、测试样本。仿真实验分析表明,基于CIPP-AHP和BP神经网络建立的高职院校专业课程思政教学评价模型具有较高评价精准度。 Based on the analysis of the influencing factors of professional curriculum ideological and political teaching in higher vocational colleges,the CIPP evaluation theory is used to construct an evaluation index system of professional curriculum ideological and political teaching in higher vocational colleges with 36 three-level indicators including professional curriculum ideological and political background,professional curriculum ideological and political input,professional curriculum ideological and political implementation,and professional curriculum ideological and political effect.AHP(Analytic Hierarchy Process)was used to determine the importance and weight of each evaluation index,and BP(back propagation)neural network was used to establish the evaluation model of ideological and political teaching of professional courses in higher vocational colleges.Seventy-five groups of expert evaluation data of ideological and political teaching of professional courses in higher vocational colleges were selected as training and test samples.The simulation results show that the evaluation model based on CIPP theory,analytic hierarchy Process and BP neural network has high evaluation accuracy.
作者 乔维德 张佳 QIAO Weide;ZHANG Jia(Wuxi Open University,Wuxi 214011,China)
机构地区 无锡开放大学
出处 《金华职业技术学院学报》 2023年第6期1-9,共9页 Journal of Jinhua Polytechnic
基金 江苏省职业教育教学改革研究重点课题“课程思政视域下专业课教师与思想政治教育工作者协同育人创新实践研究”(ZCZ57) 无锡市职业教育创新发展专项研究重点课题“高职院校‘思政课程’和‘课程思政’协同育人长效机制构建研究”(9202008)。
关键词 专业课程思政教学 CIPP 层次分析法 BP神经网络 评价模型 ideological and political teaching of professional courses CIPP analytic hierarchy process BP neural network evaluation model
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