Risk Factors and Individualized Prediction of Student Retention

Music City Center 

Student attrition is an important issue for higher education as it brings about grave costs to both students and institutions. In this project, we study two-year persistence of students enrolled at a large four-year public institution in California as First-time Freshmen from Fall 2016 to Fall 2020. Predictors considered in the study include student demographic information, socioeconomic variables, academic preparation, and their academic performance at the institution. Two analytical approaches are used, discrete-time survival analysis and random forest. The results from both models indicate that academic performance variables after enrollment are most strongly associated with two-year persistence, including term units earned, term GPA, whether a student is on probation, and whether a student earned units in the first summer after enrollment. Further, monitoring and providing help promptly to students with earned units below 6 or GPA below 2.0 in the first term may prevent them from dropping out. We also illustrate how the random forest model may be used to provide individualized prediction of two-year persistence.

student retention

discrete-time survival analysis

random forest

variable importance

individualized prediction 

Section on Statistics and Data Science Education