The Promotion of Graduate Programs through Clustering Prospective Students
The promotion of academic programs, particularly at graduate levels, emerges as a response to market changes. In general, graduate programs are not a first order necessity which makes necessary the right promotion of such programs guarantee the attraction of prospective students, which enroll in som...
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Universidad Internacional de La Rioja (UNIR)
2019-12-01
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doaj-b65dd5db81ba49589fb160ff49737a632020-11-25T02:28:43ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16601989-16602019-12-0156233210.9781/ijimai.2019.07.001ijimai.2019.07.001The Promotion of Graduate Programs through Clustering Prospective StudentsRosa María Cantón CrodaDamián Emilio Gibaja RomeroFernando-Rey Castillo-VillarThe promotion of academic programs, particularly at graduate levels, emerges as a response to market changes. In general, graduate programs are not a first order necessity which makes necessary the right promotion of such programs guarantee the attraction of prospective students, which enroll in some of them, which is essential for the financial sustainability of universities. Notably, the last one is a crucial problem for private universities. In this paper, we analyze the prospective students that enroll in a private to design better promotion strategies by using on data gathered by online sources. Specifically, we use clustering techniques to define marketing strategies based on segments of students. We find that age and city are crucial to promoting graduate programs while marital status and sex does not impact the decision of students in the university that we analyze.http://www.ijimai.org/journal/node/3223clusteringdecision-levelmachine learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rosa María Cantón Croda Damián Emilio Gibaja Romero Fernando-Rey Castillo-Villar |
spellingShingle |
Rosa María Cantón Croda Damián Emilio Gibaja Romero Fernando-Rey Castillo-Villar The Promotion of Graduate Programs through Clustering Prospective Students International Journal of Interactive Multimedia and Artificial Intelligence clustering decision-level machine learning |
author_facet |
Rosa María Cantón Croda Damián Emilio Gibaja Romero Fernando-Rey Castillo-Villar |
author_sort |
Rosa María Cantón Croda |
title |
The Promotion of Graduate Programs through Clustering Prospective Students |
title_short |
The Promotion of Graduate Programs through Clustering Prospective Students |
title_full |
The Promotion of Graduate Programs through Clustering Prospective Students |
title_fullStr |
The Promotion of Graduate Programs through Clustering Prospective Students |
title_full_unstemmed |
The Promotion of Graduate Programs through Clustering Prospective Students |
title_sort |
promotion of graduate programs through clustering prospective students |
publisher |
Universidad Internacional de La Rioja (UNIR) |
series |
International Journal of Interactive Multimedia and Artificial Intelligence |
issn |
1989-1660 1989-1660 |
publishDate |
2019-12-01 |
description |
The promotion of academic programs, particularly at graduate levels, emerges as a response to market changes. In general, graduate programs are not a first order necessity which makes necessary the right promotion of such programs guarantee the attraction of prospective students, which enroll in some of them, which is essential for the financial sustainability of universities. Notably, the last one is a crucial problem for private universities. In this paper, we analyze the prospective students that enroll in a private to design better promotion strategies by using on data gathered by online sources. Specifically, we use clustering techniques to define marketing strategies based on segments of students. We find that age and city are crucial to promoting graduate programs while marital status and sex does not impact the decision of students in the university that we analyze. |
topic |
clustering decision-level machine learning |
url |
http://www.ijimai.org/journal/node/3223 |
work_keys_str_mv |
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1724836937916743680 |