Predicting Student Dropout Using Educational Data with Temporal Convolutional Network

Student dropout is a significant concern for educational institutions globally, leading to adverse social and economic impacts. Accurately predicting dropout rates allows for timely interventions, improving student retention and success rates. TCNs are chosen for their ability to handle sequential data and capture long-range dependencies, making them well-suited for the time-series nature of educational data. The model processes information from various sources such as academic performance records, attendance logs, and demographic details, ensuring a comprehensive analysis. The implementation involves a meticulous approach to data preprocessing, including cleaning, normalization, and feature engineering to create relevant features such as average grades and attendance rates. The TCN model is then designed with optimal hyperparameters, incorporating causal and dilated convolutions to respect the temporal order of data and capture long-term dependencies. Training the model on historical data, followed by rigorous validation using metrics like accuracy, precision, recall, and F1-score, ensures its robustness and generalizability. The final model is integrated into existing student management systems to provide real-time predictions and alerts, enabling proactive identification of at-risk students. Future work aims to enhance the model's accuracy and applicability by incorporating additional data sources, such as behavioural data and socio-economic factors, and developing hybrid models that combine TCNs with LSTMs and GRUs. Efforts will also focus on improving the model's interpretability for educators and administrators, implementing real-time monitoring and adaptive learning techniques to keep the model updated with new data, and designing and testing intervention strategies based on the model's predictions to assess their effectiveness in improving student retention rates. The project aims to create a powerful tool for predicting student dropout rates, providing educational institutions with actionable insights to support student success and reduce dropout rates through timely and targeted interventions.