基于智能管理系统的职业教育发展模式

L. Ni, Qiang Huang, Jing Ye, Bin Hu, Ni Zhang, Xiangqian Chang, Zhihao Su
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引用次数: 1

摘要

以人工智能算法、云计算技术、移动互联网技术、大数据应用为核心的智能管理系统在各行业取得了显著成就。本课题设计的智能管理系统就是要运用这些技术,探索出一种适应当前互联网社会的职业教育发展模式。职业教育要以社会的专业需求为导向,以学生的职业选择为导向,以教师的技能范围为导向。本项目收集学生的学习数据,在学生的日常课堂中因材施教,为学生匹配合适的职业技术选修课和教师,并记录学生在学习过程中的反馈,以及在学生未来的职业发展中,帮助学生更好的做出职业选择。同时,收集职业教育中学生、企业、教师的需求,利用人工智能算法对学生数据和企业招聘数据进行处理,自动匹配出最适合学生和企业的组合。该系统最大的核心创新是采用了最新的深度学习算法,并根据职业教育的实际情况,对残差神经网络进行了改进,创建了适合学生专业发展建议的改进残差神经网络。最终结果表明,该算法在学生职业教育与发展过程中的辅助作用远远大于传统的职业教育管理系统,并且在C大学学生教育与职业发展数据库中,有90%左右的满意率和85%的职业匹配正确率远远高于传统职业教育管理系统的表现。
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Vocational Education Development Model Based on Intelligent Management System
Intelligent management system centered on artificial intelligence algorithms, cloud computing technology, mobile Internet technology, and big data applications have made remarkable achievements in various industries. The intelligent management system designed in this project is to apply these technologies to explore a vocational education development model that adapts to the current Internet society. Vocational education needs to be oriented to the professional needs of the society, it needs to be oriented to students' vocational choices, and it needs to be oriented to the skill range of teachers. This project collects students' learning data and teaches students in accordance with their aptitude in the course of students' daily classes, matches students with appropriate vocational and technical elective courses and teachers, and records students' feedback during their studies, and in the future career development of students, to help students make better career choices. At the same time, the needs of students, companies, and teachers in vocational education will be collected, and artificial intelligence algorithms will be used to process student data and corporate recruitment data, automatically matching the most suitable combination of students and companies. The biggest core innovation of this system is the latest deep learning algorithm, and according to the actual situation of vocational education, the residual neural network is improved to create an improved residual neural network suitable for students' professional development recommendations. The final result of this algorithm, the auxiliary role in the process of student vocational education and development is far greater than the traditional vocational education management system, and in the C University student education and professional development database, there is about 90% satisfaction rate and the 85% occupational matching accuracy rate is far higher than the performance of the traditional vocational education management system.
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