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Application of Digital Media Technology for Teaching in Higher Vocational Colleges Using Big Data

Published: 01 January 2022 Publication History

Abstract

The utilization and accessibility of big data and digital media technology by common people have decreased technological prices. Professional education has been pushed to modernize curricula and boost talent development efficiency. The vocational education curriculum system must take into account the usage of new technologies in a comprehensive manner to ensure the availability of knowledge and ability structure. To accomplish the goal, the application techniques and approaches of teaching and practice utilizing big data and digital media technologies in vocational education are explored and researched. To improve the teaching quality of higher vocational education, with the help of big data, this paper aims to conduct an in-depth study on the application of digital media technology in higher vocational education. Firstly, the teaching management system of higher vocational education is constructed. Secondly, the structure, efficient units, architecture, and database of the teaching management system are pronounced. Thirdly, fuzziness mining and teaching data scheduling are used to optimize and regulate the storage structure. According to the multiparameter optimization method, the teaching feature screening and resource scheduling are completed improving the capability of parameter recognition and online identification related to teaching resources. The simulation results show that the method proposed in this paper can make the teaching methods of higher vocational education more diverse. It is anticipated that it can help the reform process and growth of vocational education, as well as the development of innovative and entrepreneurial skills. As the data receiving rate of teaching resources is high, therefore, the ability of teaching resource scheduling is also strong along with a high application value.

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cover image Mobile Information Systems
Mobile Information Systems  Volume 2022, Issue
2022
19033 pages
ISSN:1574-017X
EISSN:1875-905X
Issue’s Table of Contents
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IOS Press

Netherlands

Publication History

Published: 01 January 2022

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