Assessing university enrollment and admission efforts via hierarchical classification and feature selection

Sebastián Maldonado, Guillermo Armelini, C. Angelo Guevara

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Recruiting prospective students efficiently and effectively is a very important challenge for universities, mainly because of the increasing competition and the relevance of enrollment-generated revenues. This work provides an intelligent system for modeling the student enrollment decisions problem. A nested logit classifier was constructed to predict which prospective students will eventually enroll in different Bachelor degree programs of a small-sized, private Chilean university. Feature selection is performed to identify the key features that influence the student decisions, such as socio-demographic variables (gender, age, school type, among others), admission efforts, and admission test results. Our results suggest that on-campus activities are far more productive than career fairs and other efforts performed off campus, demonstrating the importance of bringing prospective students to the university. Furthermore, variables such as gender, school type, and declared university and Bachelor degree program preferences are shown to be relevant in successfully modeling the student's choice of university.

Original languageEnglish
Pages (from-to)945-962
Number of pages18
JournalIntelligent Data Analysis
Volume21
Issue number4
DOIs
StatePublished - 2017

Bibliographical note

Funding Information:
This research was partially funded by the Complex Engineering Systems Institute, ISCI (ICM-FIC: P05-004-F, CONICYT: FB0816), and by CONICYT, Fondecyt projects 1160738 (first author), and 1150590 (third author).

Publisher Copyright:
© 2017 - IOS Press and the authors. All rights reserved.

Keywords

  • Hierarchical classification
  • analytics
  • feature selection
  • nested logit
  • university enrollment

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