Student Performance Prediction

Student performance prediction uses machine learning to forecast student success, aiming to identify at-risk students and personalize learning experiences. Current research emphasizes improving prediction accuracy and interpretability through various model architectures, including deep learning (e.g., neural networks, graph convolutional networks, recurrent neural networks), ensemble methods, and transfer learning, often incorporating explainable AI techniques to build trust and transparency. These advancements hold significant potential for enhancing educational interventions, optimizing resource allocation, and ultimately improving student outcomes across diverse learning environments.