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.

Embodied learning represents a natural and immersive approach to education, where the physical engagement of learners plays a critical role in how they perceive and internalize concepts. This allows students to actively embody and explore knowledge through interaction with their environment, significantly enhancing retention and understanding of complex subjects. However, researchers face significant challenges in exploring children's learning in these physically interactive spaces, particularly due to the complexity of tracking multiple students' movements and dynamic interactions in real-time. To address these challenges, this paper introduces a Double Diamond design thinking process for developing an AI-enhanced timeline aimed at assisting researchers in visualizing and analyzing interactions within embodied learning environments. We outline key considerations, challenges, and lessons learned in this user-centered design process. Our goal is to create a timeline that employs state-of-the-art AI techniques to help researchers interpret complex datasets, such as children's movements, gaze directions, and affective states during learning activities, thereby simplifying their tasks and augmenting the process of interaction analysis.

Multimodal data analysis provides profound insights into behaviors and interactions within various settings. However, the collection and analysis of this data in real-world scenarios are intricate and resource-intensive. To streamline these processes, we introduce ChimeraPy: an open-source, distributed streaming platform optimized for high-throughput data transfer across processing nodes within a computer cluster. The utility and performance of ChimeraPy are showcased through two benchmark applications, highlighting its capability to handle complex data environments.

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.