Research on Optimization Strategies of Intelligent Education Literacy of Higher Vocational Teachers under the Perspective of Big Data
- DOI
- 10.2991/978-2-38476-323-8_35How to use a DOI?
- Keywords
- Higher Vocational Teachers; Intelligent Education Literacy; Big Data
- Abstract
Nowadays, with the rapid growth of Big Data (BD) and Artificial intelligence (AI) technology, promoting Higher Vocational Education (HVEd) informatization work has already become an essential aspect of education reform, and the acceleration of the informatization process in various industries has resulted in a higher demand for Intelligent Education Literacy (IEdL) of practitioners. Higher Vocational Teachers (HVT) is an important part of the education reform in China. Higher Vocational Teachers (HVT) is the main force of talent training in Higher Vocational School (HVS) and is the core force to guarantee the high-quality development of HVS. Thus, this study explores the current level of IEdL of HVTs as well as the potential problems. Optimization improvement of the IEdL training system to enhance the IEdL of HVTs from different dimensions is finally proposed.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Xinyun Jiao AU - Yihan Ye PY - 2024 DA - 2024/12/23 TI - Research on Optimization Strategies of Intelligent Education Literacy of Higher Vocational Teachers under the Perspective of Big Data BT - Proceedings of the 2024 7th International Conference on Humanities Education and Social Sciences (ICHESS 2024) PB - Atlantis Press SP - 298 EP - 304 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-323-8_35 DO - 10.2991/978-2-38476-323-8_35 ID - Jiao2024 ER -