Assessing the Effectiveness of Ideological and Political Education Based on Big Data
- DOI
- 10.2991/978-2-38476-323-8_12How to use a DOI?
- Keywords
- Ideological and Political Instruction; Big Data; Higher Education
- Abstract
Research Overview: Evaluating Ideological and Political Education in the Big Data Era.
As information technology progresses swiftly, the integration of big data in educational settings is increasingly prevalent, offering robust backing for the precise evaluation of the efficacy of ideological and political instruction. The notion that “data should be vocal, facts should be evident” is also pertinent in the sphere of ideological and political education. According to the “Report on the Advancement of Intelligent Educational Technology in China (2019–2020),” “data-informed targeted teaching paradigms are poised to set the standard for upcoming educational trends, fostering refinement and specificity in educational practices. Big data holds extensive potential across higher education domains and serves as a strategic resource and vital catalyst for the progressive evolution of ideological and political education within academic institutions. [2]
- 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 - Li Yu AU - Wang Qi PY - 2024 DA - 2024/12/23 TI - Assessing the Effectiveness of Ideological and Political Education Based on Big Data BT - Proceedings of the 2024 7th International Conference on Humanities Education and Social Sciences (ICHESS 2024) PB - Atlantis Press SP - 93 EP - 101 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-323-8_12 DO - 10.2991/978-2-38476-323-8_12 ID - Yu2024 ER -