Information Overload in Digital Learning Environments and Academic Achievement among Libyan University Students: A Structural Equation Modeling Approach

Abstract

This study examines the complex role of information overload in shaping academic achievement within digital learning environments, with a specific focus on Libyan higher education. Drawing on data from 205 undergraduate students and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), the study investigates both the direct and indirect effects of information overload through cognitive load and self-regulated learning (SRL). The findings reveal a dual and nuanced effect. Information overload exerts a significant negative direct impact on academic achievement by increasing cognitive load and disrupting attentional processes. At the same time, it demonstrates a positive indirect effect by stimulating self-regulated learning, which partially offsets its adverse cognitive consequences. Cognitive load emerges as a key mediator that amplifies the negative pathway, while self-regulated learning functions as an adaptive mechanism that enhances academic performance. These results contribute to the literature by integrating Cognitive Load Theory and self-regulated learning within a unified empirical framework, highlighting the conditional nature of information overload in educational settings. Rather than being purely detrimental, information overload operates as a double-edged phenomenon, with its impact depending on students’ capacity to regulate their learning processes. Practically, the study underscores the need for higher education institutions—particularly in resource-constrained contexts—to move beyond information provision toward developing students’ digital literacy and self-regulation skills. Such interventions are essential for mitigating cognitive overload and fostering sustainable academic achievement in increasingly complex digital environments.