Predicting Learners’ Meta-cognition Using Eye Movements during Reading with Background Music

Many students enjoy listening to background music (BGM) when they read, but it is challenging to measure their meta-cognitive states (e.g., understanding of the passage, engagement in reading). Eye movements, as an approach in multimodal learning analytics (MmLA), can offer continuous fine-grained data that reflect learners’ cognitive processes. This study explored the potential of utilizing eye movement measures to predict learners’ meta-cognition during reading with BGM. Results showed that learners’ eye movement measures integrated with the characteristics of the BGM, learner traits, and text complexity could predict their meta-cognitive states in reading. Findings can advance our understanding of human meta-cognition in multi-channel learning settings and provide insights for personalized BGM recommendations to enhance reading experiences.