Why Do Students Drop Out? University Dropout Prediction and Associated
  Factor Analysis Using Machine Learning Techniques
Sean Kim Eliot Yoo Samuel Kim
Graduation and dropout rates have always been a serious consideration for
educational institutions and students. High dropout rates negatively impact
both the lives of individual students and institutions. To address this
problem, this study examined university dropout prediction using academic,
demographic, socioeconomic, and macroeconomic data types. Additionally, we
performed associated factor analysis to analyze which type of data would be
most influential on the performance of machine learning models in predicting
graduation and dropout status. These features were used to train four binary
classifiers to determine if students would graduate or drop out. The overall
performance of the classifiers in predicting dropout status had an average
ROC-AUC score of 0.935. The data type most influential to the model performance
was found to be academic data, with the average ROC-AUC score dropping from
0.935 to 0.811 when excluding all academic-related features from the data set.
Preliminary results indicate that a correlation does exist between data types
and dropout status.