eyeScrollR: A software method for reproducible mapping of eye-tracking data from scrollable web pages.

Area of Science:

• Human-Computer Interaction
• Cognitive Psychology
• Usability Research

Background:

• The internet is integral to modern life, driving increased use of eye-tracking technology to study online behavior.
• Mapping eye-tracking data from dynamic, scrollable web pages presents a significant methodological challenge for researchers.
• Existing methods lack the precision and standardization required for analyzing attention on web content.

Purpose of the Study:

• To introduce eyeScrollR, an R package designed to accurately map eye-tracking data from scrollable web pages.
• To provide a reproducible and reliable solution for researchers studying online attention and behavior.
• To facilitate standardized analysis of eye-tracking data within web scrolling contexts.

Main Methods:

• Development of the eyeScrollR R package utilizing a deterministic algorithm.
• Re-mapping of eye-tracking gaze coordinates to full-page coordinates using mouse scroll data.
• Incorporation of features to handle elements like sticky menus and persistent ads during scrolling.
• Validation through testing across diverse hardware, software, and web page configurations.

Main Results:

• The eyeScrollR package demonstrates high accuracy in mapping eye-tracking data when compared to manual coding.
• The package effectively handles common web page complexities, ensuring robust data mapping.
• Validation confirms the package's reliability across various testing environments.

Conclusions:

• eyeScrollR offers a reproducible and reliable method for mapping eye-tracking data from scrollable web pages.
• The package is a valuable, open-source tool for eye-tracking researchers, especially those committed to open science.
• eyeScrollR enhances the analysis of user attention and behavior in complex online environments.