Visual Noise and Cognitive Overload in Online Education: A Hidden Barrier for Pupils with Executive Function Disorders
DOI:
https://doi.org/10.15503/jecs2026.1.861.877Keywords:
cognitive load, executive functions, online education, inclusive design, visual noiseAbstract
Aim. This study examines the effect of visual noise in online learning interfaces on cognitive load and task performance among pupils with executive function disorders (EFD).
Methods. The study involved 86 Ukrainian pupils aged 10–14 diagnosed with executive function difficulties. Participants completed structured online learning tasks under two interface conditions: minimalist and visually overloaded. Cognitive load was assessed using the NASA-TLX scale, while performance was evaluated through task accuracy and completion time. Quantitative data were analysed using independent samples t-tests and effect size calculations, complemented by thematic analysis of qualitative responses.
Results. Visually overloaded interfaces produced significantly higher cognitive load scores, lower task accuracy, and longer completion times than minimalist interfaces (p < .001). Qualitative findings additionally revealed recurring patterns of sensory overstimulation, cognitive fatigue, reduced motivation, and task avoidance.
Conclusion. The findings support Cognitive Load Theory by demonstrating that excessive interface complexity exacerbates cognitive overload and negatively affects task performance in pupils with EFD. The study concludes that cognitively optimised interface design represents an essential condition for inclusive digital education.
Research restrictions. The study was limited to pupils from a single regional context and did not perform subgroup comparisons across different EFD profiles.
Cognitive value. The research provides empirical evidence on the relationship between interface complexity and cognitive overload in neurodiverse learners and contributes to the development of evidence-based inclusive design principles for digital education.
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Copyright (c) 2026 Iryna Yaroshchuk, Oksana Gomotiuk, Dáša Porubčanová, Silvia Jakabová, Katarina Minarovicova, Ahmad Moh’d Mansour

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