Research and Design of Digitally Empowered Teaching Model Transformation

Authors

  • Chengyu Wen School of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, China; Meteorological Information and Signal Processing Key Laboratory of Sichuan Higher Education Institutes of Chengdu University of Information Technology, Chengdu University of Information Technology, Chengdu 610225, China
  • Zhan Wen School of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, China; Meteorological Information and Signal Processing Key Laboratory of Sichuan Higher Education Institutes of Chengdu University of Information Technology, Chengdu University of Information Technology, Chengdu 610225, China
  • Xiaoming Zhang School of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, China
  • Wenzao Li School of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, China; Meteorological Information and Signal Processing Key Laboratory of Sichuan Higher Education Institutes of Chengdu University of Information Technology, Chengdu University of Information Technology, Chengdu 610225, China

DOI:

https://doi.org/10.53469/jrve.2024.6(10).15

Keywords:

Digital teaching model, Online platform, Deep learning model, Student-centered

Abstract

Traditional offline teaching models, particularly in courses like Data Structures and Algorithm Design, struggle to meet the demands of modern education due to limited classroom time, lack of personalized instruction, and insufficient real-time feedback. To address these issues, this paper proposes a digitally empowered teaching model that integrates big data, cloud computing, and artificial intelligence. By extending learning beyond the classroom with online platforms like MOOC, PTA, and Rain Classroom, and using a Student Concentration Analysis System, the deep learning model collects and analyzes data on students' learning behaviors. This enables personalized feedback and dynamic adjustments to teaching content, improving both engagement and learning outcomes. Additionally, a Blockchain-based Student Record System ensures secure data management, making the approach more adaptive, data-driven, and student-centered.

References

García, M., Martínez, J. A., & Pérez, R. (2022). Enhancing student engagement through digital tools: A study in higher education. Journal of Educational Technology, 15(3), 215-230.

Wang, F., & Wang, H. (2021). From traditional teaching to digital transformation: A new approach to higher education. International Journal of Educational Research, 110, 101812.

Huang, L., Zhou, Y., & Wang, J. (2023). A constructivist approach to teaching data structures in digital environments. Computers & Education, 186, 104262.

Li, S., Zhang, Y., & Chen, H. (2021). The impact of artificial intelligence on personalized learning in higher education: A systematic review. Educational Technology Research and Development, 69(1), 67-86.

Yang, J., & Li, Z. (2022). Integrating big data analytics into classroom teaching: Implications for improving student learning outcomes. Educational Technology & Society, 25(2), 94-106.

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Published

2024-10-29

How to Cite

Wen, C., Wen, Z., Zhang, X., & Li, W. (2024). Research and Design of Digitally Empowered Teaching Model Transformation. Journal of Research in Vocational Education, 6(10), 77–79. https://doi.org/10.53469/jrve.2024.6(10).15

Issue

Vol. 6 No. 10 (2024): JRVE-Volume 6 Issue 10

Section

Articles

License

Copyright (c) 2024 Chengyu Wen, Zhan Wen

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.