A Cognitive Load Theory-Based Approach to Integrating Mobile Fragmented Learning Resources

Abstract

With the rapid development of mobile internet technology, mobile fragmented learning has become a mainstream learning mode due to its convenience and flexibility. However, the vast quantity of learning resources available online often varies in quality and lacks coherent organization, leading to excessive cognitive load and reduced learning efficiency. Existing research primarily focuses on resource integration based on content similarity clustering or user behavior data, while largely overlooking learners’ cognitive characteristics and the dynamic regulation of cognitive load. As a result, current integration methods fail to meet the cognitive needs of fragmented learning. To address this issue, this study proposes a resource integration framework for mobile fragmented learning grounded in cognitive load theory. On one hand, it constructs a cognitively aligned resource analysis model using design structure matrix (DSM) to rank and categorize cognitive load elements in learning materials. On the other hand, it applies DSM-based decoupling and clustering strategies to decompose resources into manageable cognitive units and support personalized aggregation. This dual approach aims to reduce intrinsic cognitive load and enhance the efficiency of resource organization. The findings offer a cognition-informed pathway for mobile learning resource design and hold significant implications for optimizing fragmented learning experiences and advancing mobile learning theory and practice.