Online Training

Online training research focuses on improving the effectiveness and accessibility of digital learning environments, encompassing both student performance prediction and the development of novel training methodologies for AI models. Current research utilizes machine learning algorithms like random forests and deep learning architectures (CNNs, RNN-LSTMs, GCNs) to predict student performance, identify at-risk learners, and optimize AI-driven tutoring systems. These advancements hold significant implications for personalized learning, enhancing the efficiency and impact of online education across various disciplines and for diverse learners.