@Article{cmc.2020.010011,
AUTHOR = {Lei Yang, Li Feng, Liwei Tian, Hongning Dai},
TITLE = {Who Will Come: Predicting Freshman Registration Based on  Decision Tree},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {1825--1836},
URL = {http://www.techscience.com/cmc/v65n2/39909},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.010011}
}