摘要
影响思政课程教学质量的因素具有种类繁杂、数量庞大的特点,导致评价结果与实际情况的一致性较低,因此提出基于卷积神经网络的思政课程教学评价系统。以Versatile^(TM) Express为开发手段,构建以CoreTile Express板为核心的系统环境构架,在卷积神经网络中对各个学习参数进行差异化赋权,并通过学生反馈数据与预期思政教学结果之间的拟合情况实现对思政课程教学的评价。实验结果表明,设计系统评价结果的Cronbach’s α系数稳定在0.900以上,均值为0.904 9,与实际情况有较高的一致性。
The factors of teaching quality of Civics courses are characterized by a wide variety and large number, leading to a low consistency between evaluation results and the actual situation, so a convolutional neural network-based teaching evaluation system for Civics courses is proposed. Using Versatile^(TM) Express as the development means, the system environment architecture with CoreTile Express board as the core is built, the individual learning parameters are differentially assigned in the convolutional neural network, and the evaluation of Civics course teaching is realized by the fit between the student feedback data and the expected Civics teaching results. The experimental results show that the Cronbach’s α coefficient of the evaluation results of the designed system is stable above 0.900, with a mean value of 0.904 9, which has a high consistency with the actual situation.
作者
郭娟
高峰
GUO Juan;GAO Feng(Chanchun University of Artechiture and Engineer,Changchun Jilin 130000,China)
出处
《信息与电脑》
2022年第19期233-235,共3页
Information & Computer
基金
吉林省高都能教育学院会课题“吉林省高校大学生留省就业影响因素调查研究”(项目编号:JGJX2021C78)。
关键词
卷积神经网络
思政课程
系统环境构架
Cronbach’sα系数
convolutional neural network
ideological and political courses
system environment architecture
Cronbach’s α coefficient
