Learner Model

Learner modeling aims to create computational representations of student knowledge, skills, and learning processes to personalize education and improve learning outcomes. Current research emphasizes developing robust and generalizable models using techniques like Bayesian networks, large language models (LLMs), and reinforcement learning (RL), often incorporating knowledge graphs and addressing challenges like data sparsity and equitable assessment for diverse learners. These advancements hold significant potential for creating more effective and personalized educational technologies, improving assessment methods, and advancing our understanding of human learning.