Open AccessEvaluation of the Motivation Status of Enterprises and Higher Vocational Schools Participating in Modern Apprenticeship and Its Key Influencing Factors Based on Artificial Neural Network
Author(s) -
Shu Ji,
Jun Li
Publication year - 2021
Publication title -
international journal of emerging technologies in learning/international journal: emerging technologies in learning
Language(s) - English
Resource type - Journals
eISSN - 1868-8799
pISSN - 1863-0383
DOI - 10.3991/ijet.v16i08.22133
Subject(s) - apprenticeship , vocational education , construct (python library) , enthusiasm , artificial neural network , key (lock) , computer science , artificial intelligence , knowledge management , index (typography) , backpropagation , engineering management , machine learning , psychology , engineering , pedagogy , social psychology , computer security , philosophy , linguistics , world wide web , programming language
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.