GazeViz: A Web-Based Approach for Visualizing Learner Gaze Patterns in Online Educational Environment

Eduardo Davalos, Namrata Srivastava, Yike Zhang, Amanda Goodwin, Gautam Biswas

Abstract: As online learning tools become more widespread, understanding student behaviors through learning analytics is increasingly important. Traditional methods relying on system log data fall short of capturing the full range of cognitive strategies students use. To address this, we developed an in-depth post-assignment reflection dashboard that visualizes gaze data to aid students in reflecting on their learning behaviors. This dashboard was made possible by ETProWeb, a system that integrates high-fidelity eye-tracking directly into the browser, enabling real-time analysis of gaze data aligned with user interactions. ETProWeb leverages the browser's Document Object Model (DOM) to track areas of interest (AOIs) dynamically, overcoming issues related to multiple timelines and manual alignment. In a pilot study with 38 sixth-grade students, the dashboard received positive feedback, with 90% of students expressing interest in the eye-tracking technology for its ability to help them observe and reflect on their reading behaviors. This interest highlights the potential of eye-tracking as a valuable tool for enhancing students' self-awareness and engagement in online learning environments.