Cognitive Load Management in Digital Learning Environments: A Multidimensional Investigation of Instructional Design, Learner Characteristics, and Technology Affordances

Abstract

This study investigates cognitive load management in digital learning environments (DLEs) by integrating instructional design principles, learner individual differences, and technology affordances. A mixed-methods research design was employed, involving 528 undergraduate students from four U.S. universities and 12 semi-structured interviews with instructional designers. Quantitative data were collected via cognitive load assessments, academic performance tests, and self-reported surveys, while qualitative data included think-aloud protocols and interview transcripts. Results indicate that modular instructional design reduces extraneous cognitive load by 31% (p<.001) compared to linear content delivery, and learner prior knowledge moderates the relationship between technology interactivity and intrinsic cognitive load (β=-.24, p<.01). Additionally, adaptive learning technologies that adjust content complexity based on real-time learner performance significantly improve germane cognitive load engagement (d=0.82). These findings provide interdisciplinary implications for educational psychologists, cognitive scientists, and learning technology developers to optimize DLEs for diverse learner populations.