Smart Drop: A Deep Learning Framework for Predicting College Dropouts

Abstract

Educational Data Mining (EDM) plays a key role in improving modern learning by using advanced techniques. This study focuses on predicting college dropout rates with a deep learning framework. By analysing student data, our model helps identify at-risk students early, allowing timely support. The dataset contains student details such as academic performance, socioeconomic status, and engagement levels. Using these details, our framework applies deep learning methods to detect early signs of dropout. This helps universities take quick action to support students and improve their learning experience. Our study checked how well different models predicted dropout rates. The K-Nearest Neighbours (KNN) model had 93% accuracy, while the Recurrent Neural Network (RNN) had 95% accuracy. When KNN and RNN were combined, accuracy dropped to 87%. However, combining KNN and Convolutional Neural Network (CNN) improved accuracy to 97%. The RNN and CNN combination scored 96% accuracy, and the standalone CNN model also reached 97% accuracy. The best-performing method in this study is the KNN and CNN combination, which achieved the highest accuracy of 97%. Even though the CNN model alone also reached 97%, combining it with KNN helps capture more patterns in the data. This approach improves student retention by identifying and helping struggling students early. Our findings show that deep learning can effectively predict dropout rates, allowing educational institutions to provide better student support and improve success rates.