Dropout Prediction

Dropout prediction research aims to identify students at risk of leaving educational programs early, using machine learning to analyze diverse datasets encompassing academic performance, demographics, socioeconomic factors, and even engagement patterns in online learning environments. Current research emphasizes improving predictive accuracy through advanced algorithms like random forests and LSTM networks, exploring optimal data splitting strategies, and addressing challenges like data sparsity and class imbalance, particularly in federated learning settings. Accurate dropout prediction can significantly improve educational interventions, resource allocation, and ultimately, student success rates across various educational levels.