A Deep Learning Model to Predict First to Second Year Student Retention

This study investigates the application of a neural network to predict first-year student retention in engineering. The proposed algorithm predicts whether a student will return for the second academic year based on various factors, including: enrollment, ACT scores, and GPA. It examines the effects of parameters on the model's performance, such as the cohort, the semesters taken into consideration (fall only vs. fall and spring), and the academic standing of the students utilized for training and testing. The model's overall accuracy ranges from 66.7% to 95.2%, with models trained on students in strong academic standing having the highest accuracy. This variance in accuracy hampers the model's capacity to learn and forecast retention for at-risk students. To address this issue, we suggest future workarounds, such as employing data oversampling techniques or gathering data from additional years. This study adds to the research literature on the application of machine learning to the prediction of student retention. The results can help identify students at risk of not being retained and help them promptly increase their academic success toward graduation.