Teaching Practice of Big Data Technology Based on Integration of Science and Education
- DOI
- 10.2991/978-2-38476-364-1_17How to use a DOI?
- Keywords
- integration of science and education; big data technology; project-driven; teaching resources
- Abstract
Big Data Technology aims to help students master the basic mechanisms and development methods of big data distributed storage and distributed computing. The course enables students to use common tools in Hadoop and its ecosystem to realize the access, processing, calculation and analysis of big data. Through the course, students’ logical thinking and systematic thinking methods and habits of engineers would be cultivated, and the ability to analyze and solve complex engineering problems could be possessed. Therefore, taking Big Data Technology of the School of Software Engineering of Chengdu University of Information Technology as an example, two aspects of teaching resource construction supported by scientific research projects and reform of project-driven teaching methods were introduced, and the specific practice of the integration of science and education was realized. The practice provides a reference for promoting the application of the integration of science and education in higher education.
- Copyright
- © 2025 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 - Qiuyun Zhao AU - Le Wei PY - 2025 DA - 2025/03/17 TI - Teaching Practice of Big Data Technology Based on Integration of Science and Education BT - Proceedings of the 2024 4th International Conference on Education, Language and Art (ICELA 2024) PB - Atlantis Press SP - 129 EP - 134 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-364-1_17 DO - 10.2991/978-2-38476-364-1_17 ID - Zhao2025 ER -