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Abstract Although the “eye-mind link” hypothesis posits that eye movements provide a direct window into cognitive processing, linking eye movements to specific cognitions in real-world settings remains challenging. This challenge may arise because gaze metrics such as fixation duration, pupil size, and saccade amplitude are often aggregated across timelines that include heterogeneous events. To address this, we tested whether aggregating gaze parameters across participant-defined events could support the hypothesis that increased focal processing, indicated by greater gaze duration and pupil diameter, and decreased scene exploration, indicated by smaller saccade amplitude, would predict effective task performance. Using head-mounted eye trackers, nursing students engaged in simulation learning and later segmented their simulation footage into meaningful events, categorizing their behaviors, task outcomes, and cognitive states at the event level. Increased fixation duration and pupil diameter predicted higher student-rated teamwork quality, while increased pupil diameter predicted judgments of effective communication. Additionally, increased saccade amplitude positively predicted students’ perceived self-efficacy. These relationships did not vary across event types, and gaze parameters did not differ significantly between the beginning, middle, and end of events. However, there was a significant increase in fixation duration during the first five seconds of an event compared to the last five seconds of the previous event, suggesting an initial encoding phase at an event boundary. In conclusion, event-level gaze parameters serve as valid indicators of focal processing and scene exploration in natural learning environments, generalizing across event types. 

In collaborative problem-solving (CPS), students work together to solve problems using their collective knowledge and social interactions to understand the problem and progress towards a solution. This study focuses on how students engage in CPS while working in pairs in a STEM+C (Science, Technology, Engineering, Mathematics, and Computing) environment that involves open-ended computational modeling tasks. Specifically, we study how groups with different prior knowledge in physics and computing concepts differ in their information pooling and consensus-building behaviors. In addition, we examine how these differences impact the development of their shared understanding and learning. Our study consisted of a high school kinematics curriculum with 1D and 2D modeling tasks. Using an exploratory approach, we performed in-depth case studies to analyze the behaviors of groups with different prior knowledge distributions across these tasks. We identify effective information pooling and consensus-building behaviors in addition to difficulties students faced when developing a shared understanding of physics and computing concepts.