The importance of retention rate for higher education institutions has
encouraged data analysts to present various methods to predict at-risk
students. The present study, motivated by the same encouragement, proposes a
deep learning model trained with 121 features of diverse categories extracted
or engineered out of the records of 60,822 postsecondary students. The model
undertakes to identify students likely to graduate, the ones likely to transfer
to a different school, and the ones likely to drop out and leave their higher
education unfinished. This study undertakes to adjust its predictive methods
for different stages of curricular progress of students. The temporal aspects
introduced for this purpose are accounted for by incorporating layers of LSTM
in the model. Our experiments demonstrate that distinguishing between
to-be-graduate and at-risk students is reasonably achievable in the earliest
stages, and then it rapidly improves, but the resolution within the latter
category (dropout vs. transfer) depends on data accumulated over time. However,
the model remarkably foresees the fate of students who stay in the school for
three years. The model is also assigned to present the weightiest features in
the procedure of prediction, both on institutional and student levels. A large,
diverse sample size along with the investigation of more than one hundred
extracted or engineered features in our study provide new insights into
variables that affect students success, predict dropouts with reasonable
accuracy, and shed light on the less investigated issue of transfer between
colleges. More importantly, by providing individual-level predictions (as
opposed to school-level predictions) and addressing the outcomes of transfers,
this study improves the use of ML in the prediction of educational outcomes.

利用深度学习模型和 LSTM 层，研究了大规模收集的 121 种不同类别的特征对高等教育学生保留率的影响，早期对毕业和风险学生的区分能力良好，在后期对退学和转学的区分能力依赖于随时间累积的数据，提供个体层面的预测并解决转学结果问题，为教育结果预测提供了新的见解。