Date of Award

5-2016

Culminating Project Type

Thesis

Degree Name

Computer Science: M.S.

Department

Computer Science and Information Technology

College

School of Science and Engineering

First Advisor

Omar Al-Azzam

Second Advisor

David Robinson

Third Advisor

Jie Meichsner

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Keywords and Subject Headings

Retention probabilities; predictive modeling; student retention; principal component analysis; Bayesian Networks; k-Nearest Neighbor; Random Forest;

Abstract

Student graduation rates has always taken prominence in academic studies since it is considered a major factor to reflect the performance of any university. Accurate models for predicting student retention plays a major role is universities strategic planning and decision making. Students’ enrollment behavior and retention rate are also relevant factors in the measurement of the effectiveness of universities. This thesis provides a comparison of predictive models for predicting student retention at Saint Cloud State University. The models are trained and tested using a set of features reflecting the readiness of students for college education, their academic capacities, financial situation, and academic results during their freshman year. Principle Component Analysis (PCA) were used for feature selection. Six predictive models has been built. A comparison of the prediction results have been conducted using all features and selected features using PCA analysis.

Available for download on Wednesday, April 26, 2017

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