Master of Science (MS)
First Committee Member
Second Committee Member
Third Committee Member
Fourth Committee Member
Number of Pages
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.
First-year; Retention; Second-year
University of Nevada, Las Vegas
Deshmukh, Sudhir, "A Machine Learning Approach to Predict Retention of Computer Science Students at University of Nevada, Las Vegas" (2020). UNLV Theses, Dissertations, Professional Papers, and Capstones. 3887.
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