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Volume 27, Issue 2 (summer 2025)
Advances in Cognitive Sciences 2025, 27(2): 96-107 |
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Fouladlou B, Sadeghi M, Koleini Mamaghani N, Rezaei M. Evaluation of UI/UX cognitive load by tracking mouse and analyzing placement duration. Advances in Cognitive Sciences 2025; 27 (2) :96-107
URL: http://icssjournal.ir/article-1-1761-en.html
URL: http://icssjournal.ir/article-1-1761-en.html
1- MSc Graduate, Design-Creativity, Cognitive Sciences Department, Cognitive Science Institute, Tehran, Iran
2- Assistant Professor of Sensing and Measurement, Design-Creativity, Cognitive Sciences Department, Cognitive Science Institute, Tehran, Iran
3- Associate Professor, Industrial Design Department, Architecture and Urban Planning, Elm-va-Sanaat University, Tehran, Iran
4- PhD Student, Design Studies, Art Faculty, Technische Universitat Braunschweig, Braunschweig, Germany
2- Assistant Professor of Sensing and Measurement, Design-Creativity, Cognitive Sciences Department, Cognitive Science Institute, Tehran, Iran
3- Associate Professor, Industrial Design Department, Architecture and Urban Planning, Elm-va-Sanaat University, Tehran, Iran
4- PhD Student, Design Studies, Art Faculty, Technische Universitat Braunschweig, Braunschweig, Germany
Abstract: (805 Views)
Introduction: In the UI/UX design process, effective communication with users and user-friendliness are considered, which can be measured as UI/UX cognitive load. This article uses a supervised mouse-pointer-tracking method to measure user response time for evaluating cognitive load. It has assessed the pavement management dashboard case study of the Road Maintenance and Transportation Organization (RMTO) of Iran.
Methods: This is a quantitative research with simple random sampling and a statistical population of the road maintenance experts (40 people) from RMTO. A mechanism has been defined for formulating and posing questions to users and for tracking mouse pointer movement to place answer. The statistical analysis, consistent with the t-student distribution, removes outliers and incomplete datasets to calculate cognitive load and performs sensitivity analyses of the dashboard’s elements.
Results: The average data placement time in the dashboard, as a measure of cognitive load, is estimated at 15.2s. Sensitivity analysis shows that the year list filter has a high cognitive load, while the province list filter, despite greater complexity, has a lower cognitive load. This is due to the ability to filter provinces by clicking on them in the zoning map.
Conclusion: The proposed method can be applied to other UI/UX, especially dashboards, to evaluate their cognitive load. Additionally, statistical analyses such as heat mapping and A/B comparisons can be performed on the observed data.
Methods: This is a quantitative research with simple random sampling and a statistical population of the road maintenance experts (40 people) from RMTO. A mechanism has been defined for formulating and posing questions to users and for tracking mouse pointer movement to place answer. The statistical analysis, consistent with the t-student distribution, removes outliers and incomplete datasets to calculate cognitive load and performs sensitivity analyses of the dashboard’s elements.
Results: The average data placement time in the dashboard, as a measure of cognitive load, is estimated at 15.2s. Sensitivity analysis shows that the year list filter has a high cognitive load, while the province list filter, despite greater complexity, has a lower cognitive load. This is due to the ability to filter provinces by clicking on them in the zoning map.
Conclusion: The proposed method can be applied to other UI/UX, especially dashboards, to evaluate their cognitive load. Additionally, statistical analyses such as heat mapping and A/B comparisons can be performed on the observed data.
Type of Study: Research |
Received: 2025/02/20 | Accepted: 2025/09/14 | Published: 2025/10/5
Received: 2025/02/20 | Accepted: 2025/09/14 | Published: 2025/10/5
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