Abstract
Dropout refers to the phenomenon of students leaving school before completing their degree or program of study. Dropout is a major concern for educational institutions, as it affects not only the students themselves but also the institutions’ reputation and funding. Dropout can occur for a variety of reasons, including academic, financial, personal, and social factors. Therefore, understanding the factors that contribute to dropout and developing effective strategies to prevent it is a critical challenge for educational institutions. In this study, we propose a hybrid deep learning model based on Long Short-Term Memory and Deep Neural Network algorithms for school dropout prediction. The proposed model was compared with previous works and several other machine learning algorithms, including Deep Neural Network (DNN), K-Nearest Neighbors (KNN), Naive Bayes (NB), Multi-Layer Perceptron (MLP), Decision Trees (DT), Support Vector Machine (SVM), and Random Forest (RF). The results showed that the proposed DNN-LSTM model outperforms the other models in terms of accuracy and efficiency.
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All data are available from https://www.kaggle.com/datasets/thedevastator/higher-education-predictors-of-student-retention.
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El Aouifi, H., El Hajji, M. & Es-Saady, Y. A hybrid approach for early-identification of at-risk dropout students using LSTM-DNN networks. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12588-0
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DOI: https://doi.org/10.1007/s10639-024-12588-0