Who will come : Predicting freshman registration based on decision tree

Lei YANG, Li FENG*, Liwei TIAN, Hongning DAI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

The registration rate of freshmen has been a great concern at many colleges and universities, particularly private institutions. Traditionally, there are two inquiry methods: telephone and tuition-payment-status. Unfortunately, the former is not only time-consuming but also suffers from the fact that many students tend to keep their choices secret. On the other hand, the latter is not always feasible because only few students are willing to pay their university tuition fees in advance. It is often believed that it is impossible to predict incoming freshmen’s choice of university due to the large amount of subjectivity. However, if we look at the two major considerations a potential freshman contemplates in making a choice, such as the geographical location of the university in relation to his/her home town, and testimonies about of that college life experience by previous graduates, we believe it is possible to predict future enrollment decisions. This paper is the first to find a way to solve the problem of predicting the choice of university a freshman will attend. Our contributions include the following: 1. we present a dataset on freshman registration; 2. we propose a decision-tree-based approach for freshman registration prediction. Study results show that freshman registration is predictable.

Original languageEnglish
Pages (from-to)1825-1836
Number of pages12
JournalComputers, Materials and Continua
Volume65
Issue number2
DOIs
Publication statusPublished - 2020
Externally publishedYes

Bibliographical note

Funding Information:
Funding Statement: This work is funded in part by the National Nature Science Foundation of China (File Nos. 61872451 and 61872452) and in part by the Science and Technology Development Fund, Macau SAR (File Nos. 0098/2018/A3 and 0076/2019/A2). Li Feng is the corresponding author.

Publisher Copyright:
© 2020 Tech Science Press. All rights reserved.

Keywords

  • Decision tree
  • Prediction
  • Registration

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