Eye-tracking technology has expanded our ability to quantitatively measure human perception. This rich data source has been widely used to characterize human behavior and cognition. However, eye-tracking analysis has been limited in its applicability, as contextualizing gaze to environmental artifacts is non-trivial. Moreover, the temporal evolution of gaze behavior through open-ended environments where learners are alternating between tasks often remains unclear. In this paper, we propose temporal fully-weighted scanpath graphs as a novel representation of gaze behavior and combine it with a clustering scheme to obtain high-level gaze summaries that can be mapped to cognitive tasks via network metrics and cluster mean graphs. In a case study with nurse simulation-based team training, our approach was able to explain changes in gaze behavior with respect to key events during the simulation. By identifying cognitive tasks via gaze behavior, learners’ strategies can be evaluated to create online performance metrics and personalized feedback.

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.

This study presents a novel framework for 3D gaze tracking tailored for mixed-reality settings, aimed at enhancing joint attention and collaborative efforts in team-based scenarios. Conventional gaze tracking, often limited by monocular cameras and traditional eye-tracking apparatus, struggles with simultaneous data synchronization and analysis from multiple participants in group contexts. Our proposed framework leverages state-of-the-art computer vision and machine learning techniques to overcome these obstacles, enabling precise 3D gaze estimation without dependence on specialized hardware. Utilizing facial recognition and deep learning, the framework achieves real-time, tracking of gaze patterns across several individuals, addressing common depth estimation errors, and ensuring spatial and identity consistency within the dataset. This provides mechanisms for significant advances in behavior and interaction analysis in educational and professional training applications in dynamic and unstructured environments. For transparency and to promote further development, we released the code at https://github.com/edavalosanaya/3DGazeTracking_ICMIW2024.

With advancements in AI, appearance-based gaze estimation methods have improved benchmark performance, but practical deployment still lags behind commercial eye-trackers. Factors like model size, inference time, and privacy often go unaddressed. Meanwhile, webcam-based eye-tracking methods remain sensitive to head movement. To tackle these issues, we introduce WebEyeTrack, a browser-native framework that integrates a novel lightweight gaze model, metric head pose estimation, and on-device few-shot learning with as few as nine per-user calibration samples (\(k \le 9\)). WebEyeTrack adapts to new users, achieving competitive performance with an error margin of 2.32 cm on GazeCapture and real-time inference latency of 2.4 milliseconds on an iPhone 14. Our open-source code is available at github.com/RedForestAI/WebEyeTrack.