Analysis of MTSU Student Retention Data

dc.contributor.advisor Wu, Qiang Baghernejad, Danielle Marcella
dc.contributor.committeemember Calahan, Rebecca
dc.contributor.committeemember Green, Lisa
dc.contributor.committeemember Li, Cen
dc.contributor.department Basic & Applied Sciences en_US 2016-05-13T18:29:01Z 2016-05-13T18:29:01Z 2016-03-23
dc.description.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.
dc.description.abstract 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. M.S.
dc.publisher Middle Tennessee State University
dc.subject Logistic regression
dc.subject Mean imputation
dc.subject Missing values
dc.subject Multiple imputation
dc.subject Random forests
dc.subject Student retention
dc.subject.umi Mathematics
dc.subject.umi Statistics
dc.subject.umi Computer science
dc.thesis.degreegrantor Middle Tennessee State University
dc.thesis.degreelevel Masters
dc.title Analysis of MTSU Student Retention Data
dc.type Thesis
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