Construction and Practical Exploration of a Precise Management Model for Student Aid: A Multi-Dimensional Data-Driven Methodology

DOI

10.2991/978-2-38476-271-2_48How to use a DOI?

Keywords

Student Aid Management; Multidimensional Data Analysis; Gradient Boosting Decision Tree; Model Robustness

Abstract

This article aims to explore how to construct a precise management model for student funding through a multi-dimensional data-driven methodology, in order to improve funding efficiency and accuracy. This article summarizes the shortcomings of previous research and introduces a new method based on Gradient Boosting Decision Tree (GBDT). Based on the GBDT algorithm, the model achieved an accuracy of 92% and an F1 score of 90.5%. From the data conclusion, it can be seen that the GBDT model is effective in predicting student aid, and it also emphasizes the need to pay special attention to specific features and data quality before optimizing and deploying the model in the future.

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.

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TY  - CONF
AU  - Chao Wang
AU  - Xiao Lv
AU  - Yang Sun
AU  - Zhe Liu
PY  - 2024
DA  - 2024/07/29
TI  - Construction and Practical Exploration of a Precise Management Model for Student Aid: A Multi-Dimensional Data-Driven Methodology
BT  - Proceedings of the 2024 5th International Conference on Mental Health, Education and Human Development (MHEHD 2024)
PB  - Atlantis Press
SP  - 392
EP  - 397
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-271-2_48
DO  - 10.2991/978-2-38476-271-2_48
ID  - Wang2024
ER  -