Evaluating the Health of Higher Education: A Hierarchical Clustering and Multi-Model Approach

DOI

10.2991/978-94-6463-574-4_35How to use a DOI?

Keywords

Education system; Fuzzy Comprehensive Evaluation; Multiple Linear Regression Model

Abstract

To foster societal advancement through higher education improvements, we developed a model to evaluate the health of national higher education systems. Our approach began with collecting data on 13 indicators from the US, using Hierarchical Clustering to categorize these into five factors: Gender Ratio, Cost, Research & Development Funding, Academic Degrees, and Access. We applied the Entropy Weight Method to determine indicator weights and conducted a Fuzzy Comprehensive Evaluation to assess the health levels of higher education across selected nations. Further, we expanded our analysis to 13 additional countries with varying economic statuses, applying the same clustering method to assess their education health, categorized into five echelons. Specifically, we established a Multiple Linear Regression Model to identify key factors influencing its educational health. This multifaceted approach not only resolved specific research problems but also provided a robust framework for assessing and improving national higher education systems.

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  - Jiawei Kong
AU  - Jingwen Du
AU  - Chunhe Wu
PY  - 2024
DA  - 2024/11/21
TI  - Evaluating the Health of Higher Education: A Hierarchical Clustering and Multi-Model Approach
BT  - Proceedings of the 4th International Conference on Internet, Education and Information Technology (IEIT 2024)
PB  - Atlantis Press
SP  - 297
EP  - 303
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-574-4_35
DO  - 10.2991/978-94-6463-574-4_35
ID  - Kong2024
ER  -