首页> 外文期刊>International Journal of Emerging Technologies in Learning (iJET) >Evaluation of the Motivation Status of Enterprises and Higher Vocational Schools Participating in Modern Apprenticeship and Its Key Influencing Factors Based on Artificial Neural Network
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Evaluation of the Motivation Status of Enterprises and Higher Vocational Schools Participating in Modern Apprenticeship and Its Key Influencing Factors Based on Artificial Neural Network

机译:基于人工神经网络的企业和高职学校的激励状况评价及其基于人工神经网络的关键影响因素

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During the reform of talent training mode, higher vocational schools must promote and apply modern apprenticeship to meet the needs of intelligent manufacturing. However, most enterprises and schools differ greatly in the participation enthusiasm and implementation motivation for modern apprenticeship. To enhance the participation motivation, it is critical to correctly evaluate the motivation status of enterprises and schools participating in modern apprenticeship, and analyze its key influencing factors. For this reason, this paper employs the Artificial Neural Network (ANN) to evaluate such motivation status. Firstly, a Modern Apprenticeship Motivation Status (MAMS) evaluation model was established, along with its evaluation index system (EIS). Then, differences in the motivation status were compared from seven aspects. After that, an improved backpropagation (BP) neural network was built to construct and optimize the MAMS prediction model. Finally, the constructed model was proved valid through experiments.
机译:在人才培训模式改革过程中,高等职业学校必须促进和应用现代学徒,以满足智能制造的需求。然而,大多数企业和学校的参与热情和实施现代学徒的动机很大。为提高参与动机,正确评估参与现代学徒的企业和学校的动机状况至关重要,并分析其关键影响因素。因此,本文采用人工神经网络(ANN)来评估此类动机状态。首先,建立了现代学徒动机状态(MAMS)评估模型以及其评估指标体系(EIS)。然后,将动机状态的差异与七个方面进行了比较。之后,建立改进的反向化(BP)神经网络以构建和优化MAMS预测模型。最后,通过实验证明了构建的模型。

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