IEEE Access (Jan 2024)
Role, Methodology, and Measurement of Cognitive Load in Computer Science and Information Systems Research
Abstract
Cognitive load (CL), defined as the mental effort required to process information, plays a pivotal role in user performance and experience in various domains, particularly within computer science (CS) and information systems (IS). As technology grows increasingly interactive, understanding and measuring CL is crucial for designing adaptive, user-centered systems. This study investigates trends in CL measurement techniques in CS and IS research from 2017 to 2024, focusing on emerging tools, methods, and their applications. A systematic literature review (SLR) was conducted to provide a comprehensive overview of CL’s role in CS and IS, the methods used to detect it, and how it is analyzed across different tasks and environments. The motivation behind this research stems from the growing need to optimize user experiences and system efficiency through better CL management. The findings highlight a shift toward multimodal CL measurement, integrating subjective, behavioral, performance-based, and physiological data, often analyzed with machine learning in domains like human-computer interaction, education, and immersive technologies. This research highlights the importance of accurate CL measurement and suggests future directions for enhancing adaptive system design through the integration of CL metrics. Building upon these findings, future research should focus on advancing CL measurement through survey item sequencing, multimodal data integration, and device-task comparisons, while also exploring the use of AI for robust CL detection. Future research should explore survey design, multimodal data integration, device-task comparisons, and AI-based CL detection. Building on these insights, this study proposes developing non-intrusive, adaptive e-learning interfaces to optimize user engagement and personalization within LMS environments.
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