Interpreting Interaction Patterns and Cognitive Strategies in LLM-Supported Exploratory Learning: A Mixed-Methods Analysis Using the DOK Framework

Abstract

As exploratory learning (EL) is increasingly observed with the use of large language models (LLMs), students demonstrate notably varied levels of engagement and effectiveness when they interact with such LLM-supported learning environments. However, the underlying mechanisms driving these disparities, particularly in how students interact with LLMs, remain underexplored. To address this gap, this observational comparative study systematically investigates the EL strategies of 46 students in two different regions of Asia, classifying 25 distinct strategies across cognitive stages using the Depth of Knowledge model. Our analysis compares strategy usage between high and low-performing student subgroups. The findings reveal: (1) A declining trend in the utilization of EL strategies across ascending cognitive stages. (2) High AWP students employed EL strategies more frequently than their peers, with ten EL strategies exhibiting significant between-group differences. (3) Among students with different AI experience, only a few EL strategies usage and cognitive stages showed significant differences. These insights can help educators and LLM interface designers develop targeted exploratory learning assistance for different types of students and help them build high-level metacognitive processes for effective human–computer interaction.