Revealing how learning strategies emerge: a human-centered reinforcement learning perspective

Personalized learning aims to provide adaptive support that responds to individual differences during learning. While existing systems have made progress in content recommendation and task sequencing, they pay limited attention to how learners regulate their learning through strategies during interaction, which constrains support for deeper learning and self-regulation. This study examines the feasibility of modeling learning strategies using a human-centered reinforcement learning framework. It conceptualizes learning as an interactive process in which observable learning states inform strategy choices and learning outcomes provide feedback. Using large-scale learning trace data, the study maps learning states, strategies, and learning gains into an interpretable state–strategy–reward structure and evaluate the framework across multiple reinforcement learning algorithms. The results show that the framework generates differentiated strategy choices across learning states and shows stable reward improvement across algorithms. While model-generated strategies closely align with learners’ short-term outcomes, systematic differences emerge in long-term learning trajectories across learning contexts. These findings highlight the potential of reinforcement learning as a simulation tool for analyzing learning strategy dynamics in adaptive learning environments.