Exploration and Practice of Improving the Effectiveness of Financial Aid Education in Colleges and Universities Based on Big Data
- DOI
- 10.2991/978-94-6463-172-2_208How to use a DOI?
- Keywords
- Big date; Financial Support for Education; higher education; Hadoop; Visualization
- Abstract
The new aim of Financial Support for Education at colleges and universities is financial aid education. The development of big data technology has brought unprecedented development opportunities for accurate financial aid education in colleges and universities. We design and develop a big data-based analysis and visualization system for students’ behavior in colleges and universities, and construct a data-driven four-in-one financial aid education mode of “drawing, deciding, pre-determining, and determining” by mining and analyzing various information of students with financial difficulties, so as to make accurate drawings, scientific decisions, early warnings, and personalized customization for sponsored students, turn big data related financial aid into productivity and facilitate students grow and become talents. It improves the overall efficacy of financial assistance instruction.
- Copyright
- © 2023 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 - Hongli Tao PY - 2023 DA - 2023/06/30 TI - Exploration and Practice of Improving the Effectiveness of Financial Aid Education in Colleges and Universities Based on Big Data BT - Proceedings of the 2023 4th International Conference on Education, Knowledge and Information Management (ICEKIM 2023) PB - Atlantis Press SP - 1881 EP - 1888 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-172-2_208 DO - 10.2991/978-94-6463-172-2_208 ID - Tao2023 ER -