Predictive analytics models for student admission and enrollment

Jared Cirelli, Andrea M. Konkol, Faisal Aqlan, Joshua C. Nwokeji

Research output: Contribution to journalConference article

Abstract

Increasing student admission and enrollment, especially in engineering and computing programs, is a desirable goal for many universities. At the same time, this goal can be difficult to achieve. The aim of this research is to develop a data analytics model that can be used by universities and colleges to improve student admission and enrollment process. Predictive analytics is the technique of using historical data to create, test and validate a model to best describe and predict the probability of an outcome. In recent years, predictive analytics has been used in many areas including manufacturing, healthcare, and service industry. In engineering and computer science education, data analytics models can be used to describe and predict what will happen during the different stages of the enrollment process. This can help an institution determine the interventions that should be taken to support students or meet recruiting goals. In this innovative practice paper, we develop analytics models based on logistic regression, neural networks, and decision trees utilizing historical data from a local university. We focus on the analysis and modeling of student admission and enrollment data to provide a decision support for the admission staff. It may be noted, however, that this model cannot be stand alone and only serves to compliment university administrators' decision-making process to manage admissions and enrollments effectively. The developed models are tested and validated using k-fold cross validation technique.

Original languageEnglish (US)
Pages (from-to)1395-1403
Number of pages9
JournalProceedings of the International Conference on Industrial Engineering and Operations Management
Volume2018
Issue numberSEP
StatePublished - Jan 1 2018
Event3rd North American IEOM Conference. IEOM 2018 -
Duration: Sep 27 2018Sep 29 2018

Fingerprint

Students
Decision trees
Computer science
Predictive analytics
Enrollment
Admission
Logistics
Education
Decision making
Neural networks
Industry

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

@article{41222567a1004dcca19d7c2278e92667,
title = "Predictive analytics models for student admission and enrollment",
abstract = "Increasing student admission and enrollment, especially in engineering and computing programs, is a desirable goal for many universities. At the same time, this goal can be difficult to achieve. The aim of this research is to develop a data analytics model that can be used by universities and colleges to improve student admission and enrollment process. Predictive analytics is the technique of using historical data to create, test and validate a model to best describe and predict the probability of an outcome. In recent years, predictive analytics has been used in many areas including manufacturing, healthcare, and service industry. In engineering and computer science education, data analytics models can be used to describe and predict what will happen during the different stages of the enrollment process. This can help an institution determine the interventions that should be taken to support students or meet recruiting goals. In this innovative practice paper, we develop analytics models based on logistic regression, neural networks, and decision trees utilizing historical data from a local university. We focus on the analysis and modeling of student admission and enrollment data to provide a decision support for the admission staff. It may be noted, however, that this model cannot be stand alone and only serves to compliment university administrators' decision-making process to manage admissions and enrollments effectively. The developed models are tested and validated using k-fold cross validation technique.",
author = "Jared Cirelli and Konkol, {Andrea M.} and Faisal Aqlan and Nwokeji, {Joshua C.}",
year = "2018",
month = "1",
day = "1",
language = "English (US)",
volume = "2018",
pages = "1395--1403",
journal = "Proceedings of the International Conference on Industrial Engineering and Operations Management",
issn = "2169-8767",
number = "SEP",

}

Predictive analytics models for student admission and enrollment. / Cirelli, Jared; Konkol, Andrea M.; Aqlan, Faisal; Nwokeji, Joshua C.

In: Proceedings of the International Conference on Industrial Engineering and Operations Management, Vol. 2018, No. SEP, 01.01.2018, p. 1395-1403.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Predictive analytics models for student admission and enrollment

AU - Cirelli, Jared

AU - Konkol, Andrea M.

AU - Aqlan, Faisal

AU - Nwokeji, Joshua C.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Increasing student admission and enrollment, especially in engineering and computing programs, is a desirable goal for many universities. At the same time, this goal can be difficult to achieve. The aim of this research is to develop a data analytics model that can be used by universities and colleges to improve student admission and enrollment process. Predictive analytics is the technique of using historical data to create, test and validate a model to best describe and predict the probability of an outcome. In recent years, predictive analytics has been used in many areas including manufacturing, healthcare, and service industry. In engineering and computer science education, data analytics models can be used to describe and predict what will happen during the different stages of the enrollment process. This can help an institution determine the interventions that should be taken to support students or meet recruiting goals. In this innovative practice paper, we develop analytics models based on logistic regression, neural networks, and decision trees utilizing historical data from a local university. We focus on the analysis and modeling of student admission and enrollment data to provide a decision support for the admission staff. It may be noted, however, that this model cannot be stand alone and only serves to compliment university administrators' decision-making process to manage admissions and enrollments effectively. The developed models are tested and validated using k-fold cross validation technique.

AB - Increasing student admission and enrollment, especially in engineering and computing programs, is a desirable goal for many universities. At the same time, this goal can be difficult to achieve. The aim of this research is to develop a data analytics model that can be used by universities and colleges to improve student admission and enrollment process. Predictive analytics is the technique of using historical data to create, test and validate a model to best describe and predict the probability of an outcome. In recent years, predictive analytics has been used in many areas including manufacturing, healthcare, and service industry. In engineering and computer science education, data analytics models can be used to describe and predict what will happen during the different stages of the enrollment process. This can help an institution determine the interventions that should be taken to support students or meet recruiting goals. In this innovative practice paper, we develop analytics models based on logistic regression, neural networks, and decision trees utilizing historical data from a local university. We focus on the analysis and modeling of student admission and enrollment data to provide a decision support for the admission staff. It may be noted, however, that this model cannot be stand alone and only serves to compliment university administrators' decision-making process to manage admissions and enrollments effectively. The developed models are tested and validated using k-fold cross validation technique.

UR - http://www.scopus.com/inward/record.url?scp=85067037623&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85067037623&partnerID=8YFLogxK

M3 - Conference article

VL - 2018

SP - 1395

EP - 1403

JO - Proceedings of the International Conference on Industrial Engineering and Operations Management

JF - Proceedings of the International Conference on Industrial Engineering and Operations Management

SN - 2169-8767

IS - SEP

ER -