Assessing Individual Differences in Technology Enhanced Learning: The Eye-Tracking Mental Load Assessment framework

Technology-enhanced learning (TEL) integrates digital tools such as multimedia and adaptive systems to optimize educational experiences. However, TEL effectiveness depends on how well these systems align with learners’ cognitive capacities rather than on technology itself. This study introduces the Eye-Tracking Mental Load Assessment (ET-MLA) framework, designed to distinguish between perceptual and working memory load through fixation-related eye-tracking metrics. Building on cognitive load theory and the load theory of selective attention, ET-MLA combines fixation duration and fixation rate to identify variations in learners’ mental states. The framework emphasizes the importance of individual eye movement behavior profiles, acknowledging that fixation-related parameters are idiosyncratic and stable across tasks. By establishing individual baselines, ET-MLA enables adaptive TEL environments to adjust learning materials dynamically, thereby reducing extraneous cognitive load and improving instructional efficiency. This approach advances personalized and data driven educational design, contributing to more effective, cognitively aligned digital learning environments.