Student Model

Student modeling focuses on creating computational representations of student knowledge and learning behavior to personalize education and improve learning outcomes. Current research emphasizes knowledge distillation, using large, powerful "teacher" models to train smaller, more efficient "student" models, often employing techniques like contrastive learning and attention mechanisms to improve knowledge transfer and address data distribution shifts. This work is significant because it aims to create more accurate, fair, and efficient AI-driven educational tools, impacting both the development of advanced learning technologies and the understanding of human learning processes.