TY - JOUR
T1 - Role, Methodology, and Measurement of Cognitive Load in Computer Science and Information Systems Research
AU - Suryani, Mira
AU - Santoso, Harry Budi
AU - Schrepp, Martin
AU - Aji, Rizal Fathoni
AU - Hadi, Setiawan
AU - Sensuse, Dana Indra
AU - Suryono, Ryan Randy
AU - Kautsarina,
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Cognitive load
KW - computer science research
KW - information system research
KW - measurement trends
UR - http://www.scopus.com/inward/record.url?scp=85211989535&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2024.3514355
DO - 10.1109/ACCESS.2024.3514355
M3 - Article
AN - SCOPUS:85211989535
SN - 2169-3536
VL - 12
SP - 190007
EP - 190024
JO - IEEE Access
JF - IEEE Access
ER -