Beyond Evaluation: Utilizing Gaze-Based Interactions for Digital Cartography
The dissertation proposes a framework for gaze-based interactions in digital cartography and empirical testing of their effectiveness. It aims to improve task execution and user experience through innovative interactive methods.
- Year
- 2024
- Type
- Conference paper
- Original language
- EN
- Proceedings
- Proceedings of the 2024 Symposium on Eye Tracking Research and Applications
- Authors
- M. Vojtechovska
Abstract
Despite the established use of eye-tracking in cartographic evaluation, its potential for interactive purposes is underutilized. The described dissertation aims to bridge the existing gap by developing a framework that incorporates gaze-based interactions within digital cartography. Next, we aim to empirically test their effectiveness in improving task completion speed, accuracy, information recall, and user satisfaction compared to traditional controls. Preliminary results using the GazePoint HD 3 eye-tracker within a Leaflet.js map application demonstrate this approach’s technical feasibility and potential benefits. The research methodology includes a PRISMA systematic review of current gaze-based interactions in cartography, the development of a web-based technological framework, and comprehensive empirical testing of hypotheses. Gaze-based interactions in cartography could benefit sectors with intensive map reliance, such as emergency response, by enabling faster task execution or education by simplifying the teaching of complex natural or social processes through gaze-adaptive map applications. By moving eye-tracking use from evaluation to interaction, the dissertation could offer a path for enhancing decision-making, learning, and operational efficiency in a world increasingly dependent on digital mapping solutions.
Citation
Vojtechovska, M. (2024). Beyond evaluation: Utilizing gaze-based interactions for digital cartography. In Proceedings of the 2024 Symposium on Eye Tracking Research and Applications (p. 24). Association for Computing Machinery. https://doi.org/10.1145/3649902.3655102
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