Students Success Modeling: Most Important Factors

TL;DR

This study develops a deep learning model using 121 features to predict student outcomes such as graduation, transfer, or dropout, with a focus on early-stage prediction and feature importance in higher education.

Contribution

It introduces a novel LSTM-based deep learning approach that incorporates temporal data to predict individual student outcomes and transfer behavior.

Findings

Early-stage prediction of graduation and at-risk status is feasible.

Model accurately predicts student fate after three years.

Identifies key features influencing student success and transfer decisions.

Abstract

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.