Role, Methodology, and Measurement of Cognitive Load in Computer Science and Information Systems Research
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 measu...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10786995/ |
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| author | Mira Suryani Harry Budi Santoso Martin Schrepp Rizal Fathoni Aji Setiawan Hadi Dana Indra Sensuse Ryan Randy Suryono Kautsarina |
| author_facet | Mira Suryani Harry Budi Santoso Martin Schrepp Rizal Fathoni Aji Setiawan Hadi Dana Indra Sensuse Ryan Randy Suryono Kautsarina |
| author_sort | Mira Suryani |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f6bd4f4cd72a41c8afa34c34cda726d0 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f6bd4f4cd72a41c8afa34c34cda726d02025-08-20T02:32:16ZengIEEEIEEE Access2169-35362024-01-011219000719002410.1109/ACCESS.2024.351435510786995Role, Methodology, and Measurement of Cognitive Load in Computer Science and Information Systems ResearchMira Suryani0https://orcid.org/0000-0002-3259-0282Harry Budi Santoso1https://orcid.org/0000-0003-0459-0493Martin Schrepp2https://orcid.org/0000-0001-7855-2524Rizal Fathoni Aji3Setiawan Hadi4https://orcid.org/0000-0002-2929-5979Dana Indra Sensuse5https://orcid.org/0000-0002-0012-8552Ryan Randy Suryono6https://orcid.org/0000-0001-9378-8148 Kautsarina7https://orcid.org/0000-0001-5748-1763Faculty of Computer Science, Universitas Indonesia, Depok, West Java, IndonesiaFaculty of Computer Science, Universitas Indonesia, Depok, West Java, IndonesiaSAP SE, Walldorf, Baden-Württemberg, GermanyFaculty of Computer Science, Universitas Indonesia, Depok, West Java, IndonesiaFaculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang, West Java, IndonesiaFaculty of Computer Science, Universitas Indonesia, Depok, West Java, IndonesiaFaculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Bandar Lampung, Lampung, IndonesiaResearch and Human Resources Development Agency, Ministry of Communication and Information Technology of the Republic of Indonesia, Jakarta, IndonesiaCognitive 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.https://ieeexplore.ieee.org/document/10786995/Cognitive loadcomputer science researchinformation system researchmeasurement trends |
| spellingShingle | Mira Suryani Harry Budi Santoso Martin Schrepp Rizal Fathoni Aji Setiawan Hadi Dana Indra Sensuse Ryan Randy Suryono Kautsarina Role, Methodology, and Measurement of Cognitive Load in Computer Science and Information Systems Research IEEE Access Cognitive load computer science research information system research measurement trends |
| title | Role, Methodology, and Measurement of Cognitive Load in Computer Science and Information Systems Research |
| title_full | Role, Methodology, and Measurement of Cognitive Load in Computer Science and Information Systems Research |
| title_fullStr | Role, Methodology, and Measurement of Cognitive Load in Computer Science and Information Systems Research |
| title_full_unstemmed | Role, Methodology, and Measurement of Cognitive Load in Computer Science and Information Systems Research |
| title_short | Role, Methodology, and Measurement of Cognitive Load in Computer Science and Information Systems Research |
| title_sort | role methodology and measurement of cognitive load in computer science and information systems research |
| topic | Cognitive load computer science research information system research measurement trends |
| url | https://ieeexplore.ieee.org/document/10786995/ |
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