1- Institute of Cognitive Science Studies
2- Elm-o-Sanat University
3- Technische Universität Braunschweig
2- Elm-o-Sanat University
3- Technische Universität Braunschweig
Abstract: (24 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, and has evaluated the case study of the pavement management dashboard of the Road Maintenance and Transportation Organization (RMTO) of Iran.
Methodology: 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 tracking mouse pointer movement for answer placement. The statistical analysis, in consistent with the t-student distribution, removes the outliers and incomplete datasets to calculate the cognitive load and carries out the sensitivity analysis of the dashboard’s elements.
Findings: The average data placement time in the dashboard, as 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 esp. dashboards to evaluate their cognitive load. Additionally, statistical analyses such as heat mapping and A/B comparisons can be performed on the observed data
Methodology: 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 tracking mouse pointer movement for answer placement. The statistical analysis, in consistent with the t-student distribution, removes the outliers and incomplete datasets to calculate the cognitive load and carries out the sensitivity analysis of the dashboard’s elements.
Findings: The average data placement time in the dashboard, as 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 esp. 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
Received: 2025/02/20 | Accepted: 2025/09/14
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