Toward Deeper Learning: A Systematic Review of Cognitive Load Management in Mobile Learning

Abstract

Cognitive load is fundamental to learning success, but most mobile learning studies address it only at a surface level. This approach limits understanding of how instructional designs manage complexity, reduce mental burdens, and create space for deeper processing. This paper addresses this gap by reviewing 53 studies published between 2016 and 2025. Results include the identification of prevalent strategies such as autonomy, scaffolding, and practical scenarios. These practices’ associations with intrinsic and extraneous load were examined using epistemic network analysis, while germane-aligned strategies were thematically analyzed for cognitive purposes and challenges. Findings revealed that intrinsic load is primarily addressed through the structuring and sequencing of task complexity, while extraneous load emerges from how multiple instructional features are integrated within learning designs. Although germane load was excluded from most measurements, the reviewed studies consistently embedded designs aimed at promoting deeper learning. This reflects germane processing as a guiding principle for instructional design rather than a directly measured outcome. Learning achievement in mobile learning should be interpreted in relation to instructional coherence that supports germane processing and manages cognitive burdens rather than technology use alone. Alignment across technology, pedagogy, and learner agency emerges as a key design consideration that requires early decisions.