An Open-Source Approach to analyze eye Movements in CAD Users

This study focuses on analyzing eye-tracking data sought from mechanical engineering students to identify saccades and fixations using a Python-based algorithm for CAD software. The data was collected for eye movements using the Tobii Pro Nano eye tracker while students engaged in tasks that required intense visual and cognitive efforts with task using CAD software. The proposed algorithm processes the raw gaze data to pinpoint saccades and fixations, shedding light on students' visual patterns and cognitive load. The results highlight the algorithm's capability to differentiate various eye movements, which is crucial for assessing eye fatigue and productivity. This research offers a valuable tool for real-time analysis of visual attention and cognitive load in educational settings, enhancing our understanding of how students interact with visual tasks which affects the fatigue and productivity of the CAD users.