The Establishment and Analysis of the Evaluation System of Target Students’ Subdivision Group in Colleges and Universities Based on Ideal Point Algorithm
- DOI
- 10.2991/978-94-6463-172-2_18How to use a DOI?
- Keywords
- College enrollment; ideal point; comparative analysis
- Abstract
Aiming at the problem of target student source segmentation in Colleges and universities, this paper establishes a segmentation group evaluation system, selects candidate segmentation groups through comparative analysis, and uses ideal point algorithm to get the best segmentation group. Through the analysis, the established evaluation system of target student source subdivision group in Colleges and universities can do a good feasibility study on the selection decision of target student source group in Colleges and universities. The conclusion of the article has wide applicability, can guide colleges and universities to carry out enrollment publicity scientifically and reasonably, and is conducive to further optimizing the structure of student sources.
- 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 - Xin Zhao AU - Wei Guo AU - Yongli Lu AU - Tao Guo PY - 2023 DA - 2023/06/30 TI - The Establishment and Analysis of the Evaluation System of Target Students’ Subdivision Group in Colleges and Universities Based on Ideal Point Algorithm BT - Proceedings of the 2023 4th International Conference on Education, Knowledge and Information Management (ICEKIM 2023) PB - Atlantis Press SP - 153 EP - 159 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-172-2_18 DO - 10.2991/978-94-6463-172-2_18 ID - Zhao2023 ER -