Optimizing Data Intelligence Empowerment and Industry-Education Integration in University Foreign Language Teaching
- DOI
- 10.2991/978-2-38476-291-0_19How to use a DOI?
- Keywords
- College English; Teaching Reform; Data Intelligence Empowerment; Industry-Education Integration
- Abstract
In response to the challenges encountered in college English education, a novel approach to curriculum development and teaching has been proposed and implemented. This approach, emphasizing “ability prioritization, personalized instruction, data intelligence empowerment, and industry-education integration,” aims to enhance students’ foreign language application skills. By focusing on student-centered development, a personalized College English teaching method has been adopted, highlighting “practice-oriented learning, student-centered teaching, competency-driven instruction and production-oriented approaches.” This strategy seeks to optimize course quality through collaborative resource development, individualized instruction, and industry-education partnerships, ultimately benefiting the reform and innovation of university foreign language education in the modern era.
- 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 - Jingjia Guo PY - 2024 DA - 2024/09/29 TI - Optimizing Data Intelligence Empowerment and Industry-Education Integration in University Foreign Language Teaching BT - Proceedings of the 2024 3rd International Conference on Science Education and Art Appreciation (SEAA 2024) PB - Atlantis Press SP - 149 EP - 154 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-291-0_19 DO - 10.2991/978-2-38476-291-0_19 ID - Guo2024 ER -