Integrating session recording and eye-tracking: development and evaluation of a Chrome extension for user behavior analysis

Abstract

The subject of this article is the development, implementation, and evaluation of a Chrome extension designed to record and replay web sessions using integrated eye-tracking data. The goal is to develop a tool that enhances user interaction analysis by combining session recordings with eye-tracking capabilities. The tasks to be solved are as follows: create a functional Chrome extension that utilizes rrweb for session recording and WebGazer.js for eye tracking; implement features, such as session recording, replay with eye-tracking data overlays, and session management; and provide options for exporting and importing recorded sessions. The methods used are: architectural modeling using UML diagrams to design the system architecture, software engineering techniques for developing the extension, integration testing to ensure the smooth operation of combined features, and data preprocessing techniques to prevent redundancy and reduce noise in eye-tracking data. Additionally, a structured user study with detailed questionnaires combining both Likert-scale questions and open-ended responses and feedback analysis were conducted to evaluate usability and gather feedback. The following results were obtained: the extension was successfully developed and evaluated with 25 participants aged 18–35 years in a controlled environment. High usability ratings were obtained, with an average score of 4.5 out of 5 for the session recording, replay, and session management features. However, the eye-tracking feature received a lower rating of 3.8 out of 5 due to occasional inaccuracies in the eye-tracking data. The qualitative feedback indicated the usefulness of the eye-tracking feature and highlighted the need for improved data accuracy. Conclusions. The scientific novelty of this study lies in the integration of session recording and eye tracking within a Chrome extension, which represents a novel and comprehensive tool for user interaction analysis on the web. The tool's ability to capture both behavioral data and visual attention without requiring website code modifications is particularly valuable for researchers, marketing specialists, UI/UX designers, and product developers. The usability study and feedback analysis provided a clear direction for future improvements, including enhancing the eye-tracking accuracy and integrating advanced analytics and customizable reporting options. Future work will also explore the integration of machine learning algorithms to automatically analyze recorded data to provide deeper insights and actionable recommendations.