Award Date

5-1-2020

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Committee Member

Fatma Nasoz

Second Committee Member

Kazem Taghva

Third Committee Member

Laxmi Gewali

Fourth Committee Member

Magdalena Martinez

Number of Pages

85

Abstract

Student retention is an important measure when in determining student success. Retention refers to the first-time full-time student from previous fall term who returned to the same university for the following fall term. Decline in retention rate have adverse effect on stakeholders, parents, and students view about the institution, revenue generated from tuition cost and obtaining outside funds. In an effort to increase retention rates, universities have started analyzing the factors that correlate with students dropping out. Many universities have identified some of these factors and are working on developing intervention programs to help students to elevate their academic performance and eventually retain them at the university. However, identifying the students who require this intervention is a very challenging task.

In this thesis, we propose the use of machine learning models to identify students who are at-risk of not being retained so that university administration can successfully deploy intervention strategies at an early stage and prevent the students from dropping out. We implemented classification algorithms including feed forward neural networks, logistic regression, and support vector machine to determine at-risk students. The data to train these models was gathered from University of Nevada Las Vegas (UNLV) enterprise data warehouse: UNLV Analytics. The models were evaluated on various metrics and the results showed that logistic regression model performed best in predicting at-risk students for first-year retention and feedforward neural networks performed best in predicting at-risk students in second-year retention at UNLV's Department of Computer Science.

Keywords

First-year; Retention; Second-year

Disciplines

Computer Sciences

File Format

pdf

File Size

8.2 MB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

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