Student Retention Prediction

After graduating from OU, I worked with the Student Retention Team tasked with analyzing incoming student data to predict retention. We categorized a student as retained if they were still an enrolled student at the end of their first year. We collected data from students’ high school records, OU application dates, and standardized test scores for our initial predictions, and updated our models throughout the semester as data were aggregated on midterm grades, advisor comments, and student survey responses.

Most of the work I did with the lab focused on utilizing a machine-learning algorithm known as Multiple Imputation by Chained Equations (MICE) to handle some of our missing data. MICE is an algorithm capable of handling multiple columns with missing data to impute a target data point. It works by initially imputing missing data via simple imputation methods, predicting missing values of the target variable, using this model to replace our initial estimates, and iterating over this process multiple times until the imputations converge. It is a robust and flexible approach to data imputation that allows for complex relationships between variables.

Due to the confidentiality of the data, I am unable to publish any of our concrete results here.