A hybrid approach for early-identification of at-risk dropout students using LSTM-DNN networks

• Published in: Education and information technologies 2024-10, Vol.29 (14), p.18839-18857
• Main Authors: El Aouifi, Houssam, El Hajji, Mohamed, Es-Saady, Youssef
• Format: Article
• Language: English
• Subjects: Algorithms; At risk students; Computer Appl. in Social and Behavioral Sciences; Computer Science; Computers and Education; Dropout Prevention; Education; Educational Technology; Information Systems Applications (incl.Internet); Neural networks; Short Term Memory; User Interfaces and Human Computer Interaction
• ISSN: 1360-2357; 1573-7608
• DOI: 10.1007/s10639-024-12588-0

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.