Abstract
In this paper, we explored models with good performance indexes for predicting student characteristics and dropout status to prevent students from dropping out. As a result of applying 6 classification models to 30,118 academic data of University A from 2018 to 2022, the accuracy rate of XGboost algorithm was 96.9% and the recall rate was 94.4%. XGboost was selected as the final model and the importance of the dropout influencing factors was high in the following order: total number of grade changes, number of semesters completed, number of leaves of absence, grade point average, grade level, and number of academic warnings. Finally, we proposed long-term and short-term management strategies for students with a high probability of dropping out of school through a consistent dropout prediction process.
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