互联网时代高校教育人才培养模式与产教融合机制创新研究

IF 3.1 Q1 Mathematics
Yanling Wang
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引用次数: 0

摘要

摘要为了获得更好的分类评价效果,本文在卷积神经网络中加入反馈连接模型,建立了基于FCNN的高校产教融合评价模型。比较了传统BP神经网络模型和FCNN模型的MSE损失值。指标体系构建,借助卷积神经网络的准确性,围绕指标、权重和实施结果的质量进行全过程评价。以学生微表情集中识别测试数据作为学生项目参与的评价数据,对比本文提出的参与评价体系与传统参与评价体系的识别率,完成对高校教育人才培养模式的质量评价。通过对高校毕业生毕业率数据的分析,判断高校整合教育的有效性。分析显示,2022年毕业生专业匹配就业率为86.28%,体现了学校产教融合对专业应用型人才培养的高效率,产教融合机制关联良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on the Innovation of Talent Cultivation Mode and Industry-Education Integration Mechanism of College Education in the Internet Era
Abstract In this paper, in order to obtain a better classification evaluation effect, a feedback connection model is added to the convolutional neural network to establish the evaluation model of the integration of industry and education in colleges and universities based on FCNN. Compare the MSE loss values of the traditional BP neural network model and the FCNN model. Indicator system construction, with the help of the accuracy of the convolutional neural network, to carry out the whole process of evaluation around the indicators, weights, and the quality of the implementation results. The data of students’ micro-expression concentration recognition test is used as the evaluation data of students’ project participation, comparing the recognition rate of the participation evaluation system proposed in this paper and the traditional participation evaluation system to complete the quality evaluation of the talent cultivation model of college education. Analyze the data on the graduation rates of college graduates to determine the effectiveness of the university’s integration of college education. The analysis shows that in 2022, the professional matching employment rate of graduates was 86.28%, which reflects the high efficiency of the university’s industry-teaching integration on the cultivation of professional and applied talents, and the mechanism of industry-teaching integration is well affiliated.
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来源期刊
Applied Mathematics and Nonlinear Sciences
Applied Mathematics and Nonlinear Sciences Engineering-Engineering (miscellaneous)
CiteScore
2.90
自引率
25.80%
发文量
203
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