Student Learning

Student learning research currently focuses on enhancing personalization and efficiency through advanced technologies. This involves leveraging large language models for personalized learning path planning, applying machine learning to create more effective cognitive diagnosis models and adaptive learning systems, and analyzing multimodal data like eye-tracking to understand learning behaviors. These efforts aim to improve learning outcomes and address challenges such as oversmoothing in cognitive diagnosis and the potential negative impacts of AI tools on student engagement. The ultimate goal is to develop more effective and adaptable educational interventions tailored to individual student needs.