Analysis of MTSU Student Retention Data

Abstract

Student retention is a challenging task in higher education, since in general more students remaining in the university means better academic programs and higher revenue. Thus, improving retention rates can not only help current students achieve academic success, but help future students as well. The objective of this thesis is to employ data mining and predictive tools on student data to predict student retention among the freshman students. In particular, we aim to identify freshman students who are more likely to drop out so that preemptive actions can be taken by the university. Through data analysis, relevant variables are identified to incorporate into models for prediction. Missing values are taken into consideration, and missing value imputation methods are explored.

This thesis begins by introducing the theory behind missing value imputation and prediction methods before applying them on the student data set. For imputation, Mean Substitution and MI algorithms are considered, while predictive models consist of Logistic Regression and Random Forests. The final model results in the identification of several key variables, as well as areas for further study.